Business Intelligence, Analytics, Data Science, and AI, 5e (Sharda) Test Bank

Business Intelligence, Analytics, Data Science, and AI, 5th edition By Ramesh Sharda

Email: richard@qwconsultancy.com

Business Intelligence, Analytics, Data Science, and AI, 5e (Sharda) Chapter 1 An Overview of Business Intelligence, Analytics, and Data Science 1) Computerized support is used for organizational and managerial decisions that are responses to external pressures and to take advantage of market opportunities.

Answer: TRUE Diff: 1 Page Ref: 2 2) During the early days of analytics, data was often obtained from the domain experts using manual processes to build mathematical or knowledge-based models.

Answer: TRUE Diff: 2 Page Ref: 26 3) Computer applications have moved from transaction processing and monitoring activities to problem analysis and decision support applications.

Answer: TRUE Diff: 1 Page Ref: 18 4) Business intelligence is a specific term that describes a variety of information technology and data capturing focused system architectures.

Answer: FALSE Diff: 2 Page Ref: 16 5) The growth in hardware, software, and network capacities were the core contributors to the proliferation of decision support and analytics systems.

Answer: FALSE Diff: 2 Page Ref: 18 6) Managing data warehouses requires special methods, including parallel and cloud computing along with Hadoop/Spark.

Answer: TRUE Diff: 2 Page Ref: 19 7) Managing information on operations, customers, internal procedures, and employee interactions is the domain of cognitive science.

Answer: FALSE Diff: 3 Page Ref: 19 8) Decision support system (DSS), analytics, and management information system (MIS) have precise definitions agreed to by most practitioners.

Answer: FALSE Diff: 2 Page Ref: 25 9) Of all analytics types (i.e., descriptive, predictive, and prescriptive), data mining is most closely related to predictive analytics.

Answer: TRUE Diff: 1 Page Ref: 35 1 .

10) Major commercial business intelligence (BI) products and services were well established in the early 1970s.

Answer: FALSE Diff: 2 Page Ref: 25 11) Information systems that support such transactions as ATM withdrawals, bank deposits, and cash register scans at the grocery store represent online transaction processing systems (OLAP).

Answer: FALSE Diff: 3 Page Ref: 31 12) Many business users in the 1980s referred to their mainframes as "the black hole," because all the information went into it, but little ever came back, and ad hoc real-time querying was virtually impossible.

Answer: TRUE Diff: 2 Page Ref: 31 13) Data warehouses are intended to work with informational data used for online transaction processing (OLTP) systems.

Answer: TRUE Diff: 2 Page Ref: 31 14) Successful BI is a tool developed by and developed for the information systems department.

Answer: FALSE Diff: 2 Page Ref: 31 15) The time between new paradigm shifts in information systems and particularly in analytics has been getting longer.

Answer: FALSE Diff: 2 Page Ref: 27 16) Traditional BI systems use a large volume of static data that has been extracted, cleansed, and loaded into a data warehouse to produce reports and analyses.

Answer: TRUE Diff: 2 Page Ref: 32 17) Demands for instant, on-demand access to dispersed information decrease as firms successfully integrate BI into their operations.

Answer: FALSE Diff: 3 Page Ref: 21 18) Simon's decision process involves three major phases: intelligence, design, and choice.

Answer: TRUE Diff: 1 Page Ref: 20

2 .

19) The use of dashboards and data visualizations is often very effective in identifying issues and opportunities in organizations.

Answer: TRUE Diff: 2 Page Ref: 36 20) The use of statistics in baseball by the Oakland Athletics, as described in the Moneyball case study, is an example of the effectiveness of data analytics.

Answer: TRUE Diff: 2 Page Ref: 3 21) The intelligence phase of the decision process involves developing a model that represents the underlying system.

Answer: FALSE Diff: 2 Page Ref: 20 22) The goal of prescriptive analytics is to use data and machine learning tools to forecast the future values of the variables of interest.

Answer: FALSE Diff: 2 Page Ref: 36 23) Descriptive analytics is often called reporting analytics with the goal of answering the question of "what happened."

Answer: FALSE Diff: 1 Page Ref: 42 24) In the design phase of the human decision process, the problem or opportunity is defined based on all available data and information.

Answer: FALSE Diff: 2 Page Ref: 21 25) Business analytics continuum starts with predictive analytics, continues with descriptive analytics and ends with prescriptive analytics.

Answer: FALSE Diff: 2 Page Ref: 25 26) In the Opening Vignette on Sports Analytics, what was adjusted to drive one-time ticket sales?

A) Player selections B) Stadium location C) Fan tweets D) Ticket prices Answer: D Diff: 2 Page Ref: 5

3 .

27) In the Opening Vignette on Sports Analytics, what type of modeling was used to predict offensive tactics?

A) Heuristics B) Heat maps C) Cascaded decision trees D) Sentiment analysis Answer: B Diff: 3 Page Ref: 8-10 28) Computer applications have moved from transaction processing and monitoring to other activities. Which of the following is not one of those activities?

A) Problem analysis B) Solution applications C) Data monitoring D) Cloud-based technologies Answer: C Diff: 3 Page Ref: 18 29) Which of the following developments is not contributing to facilitating growth of decision support and analytics?

A) Collaboration technologies B) Data management C) Knowledge management D) Locally concentrated workforces Answer: D Diff: 3 Page Ref: 18-19 30) In what decade did separate information systems within the organizations begin to be integrated?

A) 1970s B) 1980s C) 1990s D) 2000s Answer: B Diff: 2 Page Ref: 25 31) Relational databases began to be used in which of the following time windows?

A) 1960s B) 1970s C) 1980s D) 1990s Answer: C Diff: 3 Page Ref: 25-26

4 .

32) To address the need for more versatile reporting than what was available in 1980s era, ERP led to the development of what type of system?

A) Management information systems B) Relational databases C) Executive information systems D) Data warehouses Answer: C Diff: 3 Page Ref: 26 33) Which of the following is an umbrella term that combines architectures, tools, databases, analytical tools, applications, and methodologies?

A) MIS B) DSS C) ERP D) BI Answer: D Diff: 2 Page Ref: 28 34) The competitive imperatives for BI include all of the following, except?

A) Right information B) Right user C) Right time D) Right place Answer: B Diff: 2 Page Ref: 30 35) Which of the following is not an example of transaction processing?

A) ATM withdrawal B) Bank deposit C) Sales report D) Cash register scans Answer: C Diff: 3 Page Ref: 31 36) Online transaction processing (OLTP) systems handle a company's routine ongoing business.

In contrast, a data warehouse is typically used as ________.

A) The end result of BI processes and operations B) A repository of actionable intelligence obtained from a data mart C) A distinct system that provides storage for data that will be made use of in analysis D) An integral subsystem of an online business transaction recording system Answer: C Diff: 3 Page Ref: 31-32

5 .

37) The very design that makes an OLTP system efficient for transaction processing makes it inefficient for what?

A) End-user generated ad hoc reports, queries, and analysis B) Transaction processing systems that constantly update operational databases C) The collection of reliable sources of intelligence D) Transactions such as ATM withdrawals and updating of the account balance Answer: A Diff: 2 Page Ref: 31 38) How are enterprise resources planning (ERP) systems related to supply chain management systems?

A) Different terms for the same system B) Complementary systems C) Mutually exclusive systems D) None of the above; these systems never interface Answer: B Diff: 2 Page Ref: 31 39) Which of the following is not among the systems with which BI applications are not usually integrated?

A) Databases B) Legacy systems C) Enterprise systems D) Deep learning systems Answer: D Diff: 2 Page Ref: 34 40) What has caused the growth of the demand for instant, on-demand access to dispersed information?

A) The increasing divide between users who focus on the strategic level and those who are more oriented to the tactical level B) The need to create a database infrastructure that is always online and contains all the information from the OLTP systems C) The more pressing need to close the gap between the operational data and strategic objectives D) The fact that BI cannot simply be a technical exercise for the information systems department Answer: C Diff: 3 Page Ref: 32 41) Today, many vendors offer diverse tools, some of which are completely preprogrammed (called shells). How are these shells utilized?

A) They are used for customization of BI solutions B) All a user needs to do is insert the information and data C) The shell provides a secure environment for the organization's BI data D) They host an enterprise data warehouse that can assist in decision making Answer: B Diff: 2 Page Ref: 33 6 .

42) What type of analytics is used to make decisions to achieve the best performance possible?

A) Descriptive B) Prescriptive C) Predictive D) Domain Answer: B Diff: 2 Page Ref: 36-37 43) What type of analytics seeks to determine what is likely to happen in the future?

A) Descriptive B) Prescriptive C) Predictive D) Domain Answer: C Diff: 2 Page Ref: 35 44) Which one of the following technologies is not directly related to Big Data?

A) Spark B) Hadoop C) MapReduce D) OLAP Answer: D Diff: 2 Page Ref: 37 45) Which of the following is not a phase in human decision-making process?

A) Intelligence B) Design C) Analytics D) Choice Answer: C Diff: 2 Page Ref: 20-22 46) Which of the following is a part of the design phase of the human decision-making process?

A) Formulate a model B) Search and scan the procedures C) Collect data D) Identify the problem Answer: C Diff: 2 Page Ref: 21

7 .

47) Which of the following is not among the data collection issues in the decision-making process?

A) Obtaining data may be expensive B) Data may be highly structured C) Data may be insecure D) Data may not be accurate or precise enough Answer: B Diff: 2 Page Ref: 22 48) Which one of the following is the best definition of a model?

A) Model is the exact replica of the reality B) Model is the opposite of reality C) Model is the abstraction of reality D) Model is the reality Answer: C Diff: 2 Page Ref: 23 49) Which one of the following technologies was developed after the business intelligence era?

A) Big data analytics B) Enterprise resource planning C) Relational database management systems D) Data warehousing Answer: A Diff: 2 Page Ref: 25 50) Which one of the following technologies was developed prior to the business intelligence era?

A) All of the other answers are true B) Enterprise resource planning C) Relational database management systems D) Data warehousing Answer: A Diff: 2 Page Ref: 25 51) Which one of the following is not among the data engineering challenges?

A) No real-time access B) High volumes of data C) Lack of data governance D) No clear boundaries in the data flows Answer: B Diff: 2 Page Ref: 25

8 .

52) Fundamental reasons for investing in BI must be ________ with the company's business strategy.

A) Preceded B) Contradicted C) Competed D) Aligned Answer: D Diff: 2 Page Ref: 31 53) Software instances referred to as ________ can be placed on a separate server in the network and use event- and process-based approaches to measure and monitor operational processes.

A) Subroutines B) Processors C) CPUs D) Intelligent agents Answer: D Diff: 2 Page Ref: 33 54) Organizations using BI systems are typically seeking to close the ________ between the operational data and strategic objectives has become more pressing.

A) Contract B) Deal C) Gap D) Agents Answer: C Diff: 2 Page Ref: 32 55) ________ is an umbrella term that combines architectures, tools, databases, analytical tools, applications, and methodologies.

A) ERP B) Business intelligence C) Prescriptive analytics D) Social media Answer: B Diff: 2 Page Ref: 28 56) A(n) ________ is a major component of a Business Intelligence system that holds source data.

A) ERP B) OLTP C) Software agent D) Data warehouse Answer: D Diff: 1 Page Ref: 29

9 .

57) A(n) ________ is a major component of a Business Intelligence system that is often browser based and often presents a portal or dashboard.

A) Data warehouse B) OLTP C) User interface D) ERP Answer: C Diff: 1 Page Ref: 30 58) Business ________ are now extremely compressed, faster, and more informed across industries.

A) Problems B) Challenges C) Structures D) Cycle times Answer: D Diff: 3 Page Ref: 30 59) The analytics type that provides estimation of the future values of variables of interest is often called ________ analytics.

A) Descriptive B) Predictive C) Social D) Automated Answer: B Diff: 2 Page Ref: 35 60) ________ is among the key enablers of prescriptive analytics.

A) Data mining B) Simulation C) Data warehousing D) RDBM Answer: B Diff: 2 Page Ref: 35 61) ________ providers focus on bringing all the data stores into an enterprise-wide platform.

A) Data mining B) Simulation modeling C) Hardware design D) Middleware Answer: D Diff: 2 Page Ref: 35

10 .

62) The user interface of a BI system is often referred to as a ________.

A) CRM B) Simulation C) Dashboard D) Middleware Answer: C Diff: 2 Page Ref: 38 63) Data warehouses are intended to work with informational data used for online ________ processing systems.

A) Analytical B) Transactional C) Media D) Middleware Answer: A Diff: 2 Page Ref: 31 64) With ________, all the data from every corner of the enterprise is collected and integrated into a consistent schema so that every part of the organization has access to the single version of the truth when and where needed.

A) Analytics B) ERP C) CRM D) Data warehouse Answer: B Diff: 3 Page Ref: 26 65) As the number of potential BI applications increases, the need to justify and prioritize them arises. This is not an easy task due to the large number of ________ benefits.

A) Analytics B) Quantitative C) CRM D) Intangible Answer: D Diff: 1 Page Ref: 33 66) ________ analytics help managers understand what happened and help them identify problems and opportunities.

A) Descriptive B) Social C) Predictive D) Associative Answer: A Diff: 1 Page Ref: 35

11 .

67) Accurate projections of future events and outcomes is the expected results of ________ analytics.

A) Descriptive B) Social C) Predictive D) Prescriptive Answer: C Diff: 1 Page Ref: 35 68) Well-defined business problems and opportunities is the outcome of ________ analytics.

A) Descriptive B) Social C) Predictive D) Prescriptive Answer: A Diff: 1 Page Ref: 35 69) Predicting whether the customer is likely to switch to a competitor is often called ________ analysis.

A) Competitive B) Collaborative C) Social D) Churn Answer: D Diff: 2 Page Ref: 35 70) Which of the following is not among the layers in PANDA platform?

A) Visual B) Transactional C) Analytical D) Collaborative Answer: D Diff: 3 Page Ref: 38 71) List and comment on four analytics applications in the retail value chain.

Answer: • Inventory Optimization • Price Elasticity • Market Basket Analysis • Shopper Insight • Customer Churn Analysis • Channel Analysis • New Store Analysis • Store Layout • Video Analytics Diff: 2 Page Ref: 46 12 .

72) List and define the four major components of a business intelligence system.

Answer: 1. A data warehouse, with its source data 2. Business analytics, a collection of tools for manipulating, mining, and analyzing the data in the data warehouse 3. Business performance management (BPM) for monitoring and analyzing performance 4. A user interface (e.g., a dashboard) Diff: 3 Page Ref: 33-36 73) Define OLAP and OLTP. Explain the relationship between the two.

Answer: Online transaction processing (OLTP) systems handle a company's routine ongoing business transaction, capture, storing and retrieving the relevant data often using an ERP system.

Online analytical processing (OLAP), on the other hand, is the analytics system that supports managerial decisions by using data often stored in a data warehouse. The relationship between the two is that OLTP captures the data and OLAP converts the data into information and knowledge for decision support.

Diff: 3 Page Ref: 31 74) What is the intent of the analysis of data that is stored in a data warehouse?

Answer: The intent of the analysis is to give management the ability to analyze data for insights into the business, and thus provide tactical or operational decision support whereby, for example, line personnel can make quicker and/or more informed decisions.

Diff: 2 Page Ref: 19 75) Describe prescriptive analytics. What are the problems it solves, enablers it uses, and outcomes it generates.

Answer: Prescriptive analytics is the highest echelon in analytics hierarchy where decisions are made. It answers the questions of "what should I do?" and "why should I do it?" It uses simulation, optimization, decision modeling and optimization as enablers. It produces the best possible decision for business problems or opportunities.

Diff: 2 Page Ref: 35-37 76) Business applications can be programmed to act on real-time or right-time data warehousing systems. Define and contrast these two concepts (i.e., right-time and real time); in your opinion, which one is better, justify your answer.

Answer: Real-time DW aims to update the data continuously, to keep it fresh all the time.

Whereas right-time DW adopts a data-refreshing policy based on the needed freshness of the data items. Although refreshing the DW continuously may sound like a better approach, it is significantly expensive and perhaps in most cases infeasible. Right time refresh seems to be the optimal balance between data currency and cost of DW management.

Diff: 2 Page Ref: 27

13 .

77) List and describe three levels or categories of analytics that are most often viewed as sequential and independent, but also occasionally seen as overlapping.

Answer: • Descriptive or reporting analytics refers to knowing what is happening in the organization and understanding some underlying trends and causes of such occurrences.

• Predictive analytics aims to determine what is likely to happen in the future. This analysis is based on statistical techniques as well as other more recently developed techniques that fall under the general category of data mining.

• Prescriptive analytics recognizes what is going on as well as the likely forecast and makes decisions to achieve the best performance possible.

Diff: 3 Page Ref: 35 78) List and define the phases in the human decision-making process. Provide examples for clarity.

Answer: The Intelligence Phase: Problem (or opportunity) identification The Design Phase: Modeling and alternative generation The Choice Phase: Selecting the best alternatives from the available ones (involving a multicriteria multi-objective decision making process) The Implementation Phase: Putting the selected solution into action, also involved change management and user training.

Diff: 2 Page Ref: 22-25 79) Describe and define Big Data. Why is a search engine a Big Data application?

Answer: • Big Data is data that cannot be stored in a single storage unit. Big Data typically refers to data that is arriving in many different forms, be they structured, unstructured, or in a stream. Major sources of such data are clickstreams from Web sites, postings on social media sites such as Facebook, or data from traffic, sensors, or weather.

• A Web search engine such as Google needs to search and index billions of Web pages in order to give you relevant search results in a fraction of a second. Although this is not done in real time, generating an index of all the Web pages on the Internet is not an easy task.

Diff: 3 Page Ref: 27 80) List the evolution of analytics names (buzzwords) since 1970s. Why do you think the name of analytics kept changing?

Answer: 1970s: DSS, 1980s: ERP, 1990s: EIS, 2000s: BI, 2010: Business Analytics/Big Data/Data Science, 2020: AI/Deep Learning/Automation/Robotics. The reason for name change is (1) to keep the field vibrant/exciting, and (2) to capture and explicitly delineate the inclusion/enrichment of analytics methods and technologies.

Diff: 3 Page Ref: 25-26

14 .

Business Intelligence, Analytics, Data Science, and AI, 5e (Sharda) Chapter 2 Artificial Intelligence: Concepts, Drivers, Major Technologies, and Business Applications 1) We like to see computerized decision-making being simpler, easier to use, more intuitive, and less threatening.

Answer: TRUE Diff: 1 Page Ref: 65 2) According to CIO Insight magazine, intelligent computer technologies will include selfinitiating, creative robots, and fully automated decisioning systems.

Answer: FALSE Diff: 2 Page Ref: 65 3) Many experts agree that AI is concerned with two basic ideas: (1) the study of human thought processes (to understand what intelligence is) and (2) the representation and duplication of those thought processes in machines.

Answer: TRUE Diff: 2 Page Ref: 66 4) One well-publicized definition of AI is "the capabilities of a machine to achieve creatively intelligent human behavior."

Answer: FALSE Diff: 1 Page Ref: 66 5) Most of the existing AI applications, which are between assisted and autonomous, are referred to as directed intelligence.

Answer: FALSE Diff: 2 Page Ref: 70 6) Lack of human touch is considered as one of the limitations of AI machines.

Answer: TRUE Diff: 2 Page Ref: 69 7) One of the benefits of AI in business recruitment is its ability to conduct more efficient and standardized candidate screening.

Answer: TRUE Diff: 1 Page Ref: 69 8) AI has many benefits and limitations. A benefit of AI machines that people often list is their lack of attention to non-task surroundings.

Answer: FALSE Diff: 2 Page Ref: 69

1 .

9) Although intelligence is usually measured by an IQ test, it may not be an optimal measure since there are several different types of intelligence.

Answer: TRUE Diff: 2 Page Ref: 71 10) To understand what human intelligence is, it is useful to first examine those abilities that are considered signs of machine intelligence.

Answer: FALSE Diff: 2 Page Ref: 72 11) Turing test aims to quantitatively measure the level of intelligence in humans.

Answer: FALSE Diff: 3 Page Ref: 72 12) Intelligent agents do not have the ability to learn by using and expanding the knowledge embedded in them.

Answer: FALSE Diff: 2 Page Ref: 75 13) Intelligent agents are effective tools for overcoming the most critical burden of the Internet information overload and making computers more viable decision support tools.

Answer: TRUE Diff: 2 Page Ref: 75 14) Highly complex, creativity requiring decisions are more likely to be fully automated by AI and intelligent agents.

Answer: FALSE Diff: 2 Page Ref: 80 15) Augmented reality is the application of knowledge derived from cognitive science (the study of the human brain).

Answer: FALSE Diff: 2 Page Ref: 79 16) Many AI systems are based on business or other types of rules. The quality of automated decisions depends on the quality of these rules.

Answer: TRUE Diff: 2 Page Ref: 80 17) Some decisions cannot be automated because it takes too much time to get all the relevant input data.

Answer: TRUE Diff: 2 Page Ref: 80 18) The process of automated decision making starts with knowledge refinement.

Answer: FALSE Diff: 2 Page Ref: 81 2 .

19) Integration of digital information with the user environment in real time (mostly vision and sound) is often called augmented reality.

Answer: TRUE Diff: 2 Page Ref: 79 20) AI and analytics will automate many of the routine tasks done today by accountants, some of whom may lose their jobs.

Answer: TRUE Diff: 2 Page Ref: 84 21) AI activities and applications in the banking industry have been solely focused on backoffice operations.

Answer: FALSE Diff: 2 Page Ref: 84 22) AI machines provide speed, accuracy, and efficiency in performing insurance claims, but cannot assist in the underwriting process.

Answer: FALSE Diff: 2 Page Ref: 85 23) Descriptive analytics is often called reporting analytics with the goal of answering the question of "what happened."

Answer: TRUE Diff: 1 Page Ref: 42 24) Every robotics scientist has the same view about the definition of robot: an AI enhanced software and hardware that looks like a human.

Answer: FALSE Diff: 2 Page Ref: 88 25) Autonomous cars are considered as robots in motion.

Answer: TRUE Diff: 1 Page Ref: 100 26) Which of the following is not among the foundational enablers of AI? A) Neurology B) IoT C) Intelligent agents D) Statistics Answer: C Diff: 3 Page Ref: 67

3 .

27) Which of the following was not included in the technologies listed as intelligent ones by CIO Insight?

A) Deep machine learning B) IoT C) Advanced robotics D) Self-driven vehicles Answer: A Diff: 2 Page Ref: 65 28) All of the following are among the technologies and applications of AI, except?

A) Augmented reality B) IoT C) Smart factories D) Robo advisers Answer: B Diff: 2 Page Ref: 67 29) The overall goal of AI is to create ________ that are capable of executing a variety of tasks currently done by people.

A) Humanoid B) Framework C) Decision trees D) Intelligent machines Answer: D Diff: 1 Page Ref: 67 30) The use of AI has been driven by all of the following forces, except?

A) The low cost of AI applications versus the high cost of manual labor B) To outsource time demanding manual labor to offshore companies C) The development of new technologies, particularly cloud computing D) The pressure on management to increase productivity and speed Answer: B Diff: 2 Page Ref: 68 31) Which of the following is not among the major benefits of AI? A) AI systems allow for continuous improvement projects B) AI can manage delays and blockages in business processes C) AI machines can learn and improve their performance D) Emotional barriers can interfere with AI work Answer: D Diff: 1 Page Ref: 68

4 .

32) Which of the following is not among the limitations of AI machines?

A) They lack human touch and feel B) They lack attention to non-task surroundings C) They can learn and improve their performance D) They can lead people to rely on AI machines too much Answer: C Diff: 2 Page Ref: 69 33) Which of the following is not a true statement about AI machines and their limitations?

A) AI machines limitations are increasing with time B) AI machines can be programmed to create destruction C) AI machine can start to think by themselves, causing significant damage D) AI machine may cause some people to lose their jobs Answer: A Diff: 3 Page Ref: 69 34) Which of the following is not among the capability levels of AI systems?

A) Assisted intelligence B) Augmented intelligence C) Augmented reality D) Autonomous AI Answer: C Diff: 2 Page Ref: 70 35) ________ can extend human cognitive abilities, resulting in high performance.

A) Artificial intelligence B) Augmented intelligence C) Augmented reality D) Autonomous AI Answer: B Diff: 3 Page Ref: 70 36) Which of the following is not accurate about augmented machines?

A) It can extend human thinking capabilities B) It excels in solving complex human and industry problems C) It provides insights and recommendations, including explanations D) It can act autonomously on behalf of knowledge workers Answer: D Diff: 2 Page Ref: 71 37) Intelligence is usually measured by which of the following tests?

A) Sensitivity test B) IQ test C) Specificity test D) Cognition test (CQ) Answer: B Diff: 1 Page Ref: 71 5 .

38) Which of the following is not a type of intelligence?

A) Linguistic and verbal B) Logical C) Physical D) Musical Answer: C Diff: 2 Page Ref: 71 39) The concept of intelligence is not simple; it is composed of all of the following, except?

A) Reasoning B) Perception C) Sensing D) Learning Answer: C Diff: 2 Page Ref: 72 40) Following are abilities that are considered signs of human intelligence, except?

A) Recognizing and judging the relative importance of different elements in a situation B) Inferring in an irrational way while solving complex problems C) Responding quickly and successfully to a new situation D) Applying knowledge to manipulate environments and situations Answer: B Diff: 1 Page Ref: 72 41) The ________ is a well-known attempt to measure the intelligence level of AI machines.

A) Competency test B) IQ test C) Turing test D) Cognitive test Answer: C Diff: 2 Page Ref: 72 42) In comparing AI versus human intelligence (HI), which of the following is not true?

A) AI executed faster than HI B) Consistency of AI is better than HI C) AI is flexible while HI is rigid D) AI is uninspired while HI is truly creative Answer: C Diff: 3 Page Ref: 73

6 .

43) ________ is a relatively small software program that observes and acts upon changes in its environment by running specific tasks autonomously.

A) Augmented AI systems B) Decision support tool C) Intelligent agent D) Machine learning Answer: C Diff: 2 Page Ref: 75 44) Machine learning is modeled after the learning methods of ________.

A) Computers B) AI C) Humans D) Nature Answer: C Diff: 2 Page Ref: 75 45) Deep learning uses ________ and plays a major role in dealing with complex applications that regular machine learning and other AI technologies cannot handle.

A) Support vector machines technologies B) Artificial neural technology C) Decision tree technologies D) Ensemble modeling technologies Answer: B Diff: 2 Page Ref: 76 46) Which one of the following is not among the technologies currently benefiting from deep learning?

A) Smartphones B) Robotics C) Autonomous vehicles D) Sensor networks Answer: D Diff: 3 Page Ref: 76 47) Deep learning is mostly useful in real-time interactive applications in the areas of all of the following, except?

A) Reconnaissance B) Machine vision C) Robotics D) Speech and voice processing Answer: A Diff: 3 Page Ref: 20-22

7 .

48) ________ includes the technology and methods used to provide imaging-based automated inspection and analysis for applications such as robot guidance, process control, and autonomous vehicles.

A) Machine learning B) Deep learning C) Machine vision D) AI automation Answer: C Diff: 2 Page Ref: 76 49) What is the difference between machine vision and computer vision?

A) Machine vision is an engineering subfield, while computer vision belongs to computer science B) Machine vision is about detecting, while computer vision is about sensing C) Machine vision is more accurate in recognition of images, while computer vision is more accurate in detecting objects D) There is no difference, they are the same Answer: A Diff: 3 Page Ref: 76 50) Which one of the following is not among the AI computer vision applications?

A) Examine and identify wood B) Identify illegal poachers of animals C) Facial recognition D) Exploration and identification of new stars Answer: D Diff: 2 Page Ref: 77 51) ________ is a derivative application of computer vision that enables the recognition of dynamic patterns and identifying events.

A) Predictive analytics B) Social analytics C) Video analytics D) Behavioral analytics Answer: C Diff: 2 Page Ref: 77 52) ________ is a technology that gives users the ability to communicate with a computer in their native language.

A) Computational linguistic B) Natural language processing C) Human perception D) Deep learning Answer: B Diff: 1 Page Ref: 77

8 .

53) Which one of the following is/are the natural language subfields?

A) Natural language generation B) Natural language understanding C) Both natural language generation and understanding D) None of the other answers Answer: C Diff: 2 Page Ref: 77 54) Requirements of knowledge acquisition include all of the following, except?

A) Identification of the security and privacy issues B) Identification of the type if knowledge C) Identification of the sources of the knowledge D) Identification of the feasibility of acquiring the knowledge Answer: A Diff: 2 Page Ref: 78 55) The knowledge representation methods depend on all of the following, except?

A) What the knowledge will be used for B) Hardware and software limitations of the knowledge-based systems C) How the reasoning from this knowledge will be done D) How users will interact with the knowledge Answer: B Diff: 3 Page Ref: 78 56) The intelligent system feature that processes users' requests and provides answers (e.g., solutions, recommendations) to the user is called ________.

A) Knowledge acquisition B) Knowledge representation C) Reasoning from knowledge D) Knowledge refining Answer: C Diff: 2 Page Ref: 79 57) ________ is the application of knowledge derived from the study of the human brain and computer science theories in order to simulate human thought processes.

A) Deep learning B) Shallow learning C) Computational linguistics D) Cognitive computing Answer: D Diff: 3 Page Ref: 79

9 .

58) ________ is the integration of digital information with the user environment in real time (mostly vision and sound) to provide users interactive experience with the environment.

A) Virtual reality B) Augmented reality C) Immersive reality D) Deep reality Answer: B Diff: 2 Page Ref: 79 59) Which of the following is not among the steps of the decision-making process that does not get support from AI? A) Selecting a solution B) Identification of problems C) Integration of disparate information sources D) Generating alternative solutions Answer: C Diff: 2 Page Ref: 81 60) Which of the following is not among the examples of decisions made automatically by rulebased expert systems?

A) Small loan approvals B) Initial screening of job applicants C) Automatic pricing of products and services (when and how to change them) D) Automatic detection of homeland security threats Answer: D Diff: 2 Page Ref: 81 61) The process automated decision making starts with which of the following?

A) Knowledge refinement B) Knowledge representation C) Knowledge translation D) Knowledge acquisition Answer: D Diff: 2 Page Ref: 81 62) AI activities in financial services include all of the following, except?

A) Extreme personalization by using chatbots B) Recruiting and acquiring customers C) Facilitating trust in digital identity D) Offering financial services 24/7 and globally Answer: B Diff: 2 Page Ref: 84

10 .

63) Which of the following is not among the examples of banking institutions' use of AI? A) Employee surveillance B) Tax preparation C) Automatically answering account related queries D) Ensuring compliance to federal regulations Answer: D Diff: 2 Page Ref: 83 64) Following are among the reasons for the use of AI in human resource management, except?

A) Reducing human bias in recruiting B) Increasing efficiency, productivity, and insight in evaluating job candidates C) Eliminating inequalities in pay rates D) Improving relationships with current employees Answer: C Diff: 2 Page Ref: 85 65) Which of the following is not among the applications of AI in human resource management?

A) Recruitment B) Training C) Retention D) Enrichment Answer: A Diff: 2 Page Ref: 31 66) Following are all examples to AI technologies used in marketing, except?

A) AI recruit new customers and customer orders B) AI mimics the expertise of in-store salespeople C) AI provides lead generation D) AI increases customer loyalty using personalization Answer: A Diff: 2 Page Ref: 86 67) ________ is a machine or a physical device or software or any combination of these three that with the cooperation of AI can accomplish a responsibility autonomously.

A) Analytics B) Business intelligence C) Robot D) Cognition Answer: C Diff: 1 Page Ref: 88

11 .

68) Which of the following is not true about robots?

A) Robots can be fully autonomous or can be remotely controlled by a human B) Androids are robots that resemble humans C) Robots has true intelligence to create new knowledge and behavior much like human do D) Autonomous robots are equipped with AI intelligent agents Answer: C Diff: 3 Page Ref: 89 69) Which of the following is not among the key issues with self-driving cars?

A) Lack of public trust B) Safety challenges C) Regulatory challenges D) Environmental challenges Answer: B Diff: 2 Page Ref: 101 70) Which of the following is not one of the service industries benefiting from enterprise chatbots?

A) Pharmaceutical B) Hospitality C) Healthcare D) Education Answer: A Diff: 1 Page Ref: 107-108 71) What is AI? List and discuss the drivers of AI. Answer: AI is "the capabilities of a machine to imitate intelligent human behavior."

The use of AI has been driven by the following forces: • People's interest in smart machines and artificial brains • The low cost of AI applications versus the high cost of manual labor (doing the same work) • The desire of large tech companies to capture competitive advantage and market share of the AI market and their willingness to invest billions of dollars in AI • The pressure on management to increase productivity and speed • The availability of quality data contributing to the progress of AI • The increasing functionalities and reduced cost of computers in general • The development of new technologies, particularly cloud computing Diff: 3 Page Ref: 66, 68 72) What is the Turing test? Who came up with it? Describe the process of performing it.

Answer: Turing test is developed by American Computer Scientist, Allan Turing. According to this test, a computer can be considered smart only when a human interviewer asking the same questions to both an unseen human and an unseen computer cannot determine which is which.

This test is limited to a question-and-answer (Q&A) mode. To pass the Turing Test, a computer needs to be able to understand a human language (NLP), to possess human intelligence (e.g., have a knowledge base), to reason using its stored knowledge, and to be able to learn from its experiences (machine learning).

Diff: 3 Page Ref: 72-74 12 .

73) Discuss some of the issues and factors that would drive the justification decision to successfully use AI. Answer: Several issues determine the justification of using AI and its chance of success. These include: • The nature of the decision. For example, routine decisions are more likely to be fully automated, especially if they are simple.

• The method of support, what technology(ies) is (are) used. Initially, automated decision support was rule-based. Practically, expert systems were created to generate solutions to specific decision situations in well-defined domains. Another popular technology mentioned earlier was "recommender," which appeared with e-commerce in the 1990s. Today, there is an increased use of machine learning and deep learning. A related technology is that of pattern recognition.

Today, attention is also given to biometric types of recognition.

Diff: 2 Page Ref: 80 74) Define intelligent agents. How do intelligent agents learn and function? Provide and explain an example to intelligent agents.

Answer: An intelligent agent (IA) is an autonomous, relatively small computer software program that observes and acts upon changes in its environment by running specific tasks autonomously. An IA directs an agent's activities to achieve specific goals related to the changes in the surrounding environment. Intelligent agents may have the ability to learn by using and expanding the knowledge embedded in them.

Example: A simple example of an intelligent software agent is a virus detection program. It resides in a computer, scans all incoming data, and removes found viruses automatically while learning to detect new virus types and detection methods.

Diff: 2 Page Ref: 75 75) What is machine learning and what is deep learning? What is the relationship between machine learning and deep learning?

Answer: At this time, AI systems do not have the same learning capabilities that humans have; rather, they have simplistic (but improving) machine learning (modeled after human learning methods. Machine-learning scientists try to teach computers to identify patterns and make connections by showing the machines a large volume of examples and related data.

Deep learning is a part of machine learning (more advanced version of machine learning) that uses artificial neural technology and plays a major role in dealing with complex applications that regular machine learning and other AI technologies cannot handle.

Diff: 2 Page Ref: 75-76 76) What is natural language processing (NLP)? List and briefly describe NLP's two key subfields.

Answer: NLP is a technology that gives users the ability to communicate with a computer in their native language. The two subfields of NLP are: 1-Natural language understanding that investigates methods of enabling computers to comprehend instructions or queries provided in ordinary English or other human languages.

2-Natural language generation that strives to have computers produce ordinary spoken language so that people can understand the computers more easily.

Diff: 2 Page Ref: 77 13 .

77) How does AI support the human decision-making process?

Answer: Problem Identification - AI systems are used extensively in problem identification typically in diagnosing equipment malfunction and medical problems, finding security breaches, estimating financial health, and so on. Several technologies are used. For example, sensorcollected data are used by AI algorithms. Performance levels of machines are compared to standards, and trend analysis can point to opportunities or troubles.

Generating or Finding Alternative Solutions - Several AI technologies offer alternative solutions by matching problem characteristics with best practices or proven solutions stored in databases. Both expert systems and chatbots employ this approach. They can generate recommended solutions or provide several options from which to choose. AI tools such as casebased reasoning and neural computing are used for this purpose.

Selecting a Solution - AI models are used to evaluate proposed solutions, for example, by predicting their future impact (predictive analysis), assessing their chance of success, or predicting a company's reply to action taken by a competitor.

Implementing the Solutions - AI can be used to support the implementation of complex solutions. For example, it can be used to demonstrate the superiority of proposals and to assess resistance to changes.

Diff: 3 Page Ref: 81 78) List and briefly define the areas in human resource management where AI can help.

Answer: Recruitment (Talent Acquisition) - Companies that help recruiters and job seekers, especially LinkedIn, are using AI algorithms to suggest matches to both recruiters and job seekers.

Facilitate Training - The rapid technological developments make it necessary to train and retrain employees. AI methods can be used to facilitate learning. For example, chatbots can be used as a source of knowledge to answer learners' queries.

Supports Performance Analysis (Evaluation) - AI tools enable HR management to conduct performance analysis by breaking work into many small components and by measuring the performance of each employee and team on each component.

Retention and Attrition Detection - In order to keep employees from leaving, it is necessary for businesses to analyze and predict how to make workers happy. Machine learning can be used to detect reasons why employees leave companies by identifying influencing patterns.

Diff: 3 Page Ref: 85-86

14 .

79) List and explain at least four uses of AI in marketing.

Answer: 1. AI can be used to mimic the expertise of in-store salespeople. In many physical stores, humans are not readily available to help customers who do not want to wait very long. Thus, shopping is made easier when bots provide guidance. A Japanese store already provides all services in a physical store by speaking robots.

2. AI can provide lead generation. As seen in the case of Einstein, AI can help generate sales leads by analyzing customers' data. The program can generate predictions. Insights can be generated by intelligent analytics.

3. AI can increase customer loyalty using personalization. For example, some AI techniques can recognize regular customers (e.g., in banks).

4. AI can improve the sales pipeline. Companies can use AI and robots to convert unknown visitors into customers.

Diff: 2 Page Ref: 85-86 80) Describe and discuss the capabilities of the following known robots: Pepper, Da Vinci, AgroBot. Answer: Pepper is a semi-humanoid robot manufactured by SoftBank Robotics that can understand human emotions. A screen is located on its chest. It can identify frowning, tone of voice, smiling, and user actions such as the angle of a person's head and crossed fingers. It has been successfully deployed in a variety of human facing tasks.

Da Vinci is a robot that is used in surgical procedures. Over the last decade, the use of robotics has emerged in surgeries. Da Vinci is designed to perform numerous nominally invasive operations and can perform simple as well as complex and delicate surgeries.

AGROBOT, a company engaged in the business of agricultural robots, has developed a robot that can harvest strawberries at any place. Robots using 24 robotic manipulators built on a mobile platform work to identify superior quality strawberries.

Diff: 2 Page Ref: 98-99

15 .

Business Intelligence, Analytics, Data Science, and AI, 5e (Sharda) Chapter 3 Descriptive Analytics I: Nature of Data, Big Data, and Statistical Modeling 1) To respond to the market challenges, SiriusXM decided to focus on production efficiency.

Answer: FALSE Diff: 1 Page Ref: 117 2) Data is the contextualization of information, also called "information set in context."

Answer: FALSE Diff: 1 Page Ref: 121 3) Data is the main ingredient, or row material, for any BI, data science, and business analytics initiative.

Answer: TRUE Diff: 2 Page Ref: 121 4) Predictive algorithms generally require a flat file with a target variable, so making data analytics ready for prediction means that data sets must be transformed into a flat-file format and made ready for ingestion into those predictive algorithms.

Answer: TRUE Diff: 2 Page Ref: 122 5) Data source reliability means that data are correct and are a good match for the analytics problem.

Answer: FALSE Diff: 1 Page Ref: 123 6) Data accessibility means that the data are easily and readily obtainable.

Answer: TRUE Diff: 3 Page Ref: 59 7) Structured data is what data mining algorithms use and can be classified as categorical or numeric.

Answer: TRUE Diff: 1 Page Ref: 125 8) Interval data are numerical variables that have an absolute zero reference point.

Answer: FALSE Diff: 2 Page Ref: 126 9) Nominal data is unstructured because they are not represented with numbers.

Answer: FALSE Diff: 2 Page Ref: 125 10) Ordinal data is a sub-part of the numerical data category within the data taxonomy.

Answer: FALSE Diff: 2 Page Ref: 125 1 .

11) Unstructured data includes multimedia data, textual data, and web log data.

Answer: TRUE Diff: 2 Page Ref: 125 12) Datum is what we name the plural form of data.

Answer: FALSE Diff: 2 Page Ref: 125 13) Data are often viewed as the lowest level of abstraction from which information and then knowledge is derived.

Answer: TRUE Diff: 2 Page Ref: 125 14) Data preprocessing is a tedious and time-demanding process necessary to convert the raw real-world data into a well-refined form for analytics algorithms.

Answer: TRUE Diff: 2 Page Ref: 127 15) In the first phase of data preprocessing, the data is cleaned for further transformation.

Answer: FALSE Diff: 2 Page Ref: 128 16) Big data is defined solely by its sheer volume, which can be at the Exabyte or Zettabyte levels.

Answer: FALSE Diff: 2 Page Ref: 137 17) A clear business need is amongst the most important big data analytics success factors.

Answer: TRUE Diff: 2 Page Ref: 141 18) Both commercial and opensource MapReduce libraries are available that provide a wide range of analytic capabilities.

Answer: TRUE Diff: 2 Page Ref: 148 19) Hadoop is a big data technology that distributes the analytics processing of very large multistructured data files across a large cluster of machines.

Answer: FALSE Diff: 2 Page Ref: 141 20) The main benefit of Hadoop is that it allows enterprises to process and analyze large volumes of unstructured and semi-structured data in a cost and time-effective manner.

Answer: TRUE Diff: 2 Page Ref: 147

2 .

21) Spark is faster than Hadoop because it uses random access memory (RAM) instead of reading and writing intermediate data to disks.

Answer: TRUE Diff: 2 Page Ref: 150 22) Data for good aims to use mostly publicly available data sources and business analytics and data science tools and techniques to create new understanding, awareness, and solutions to problems that affect the environment, large populations, and often less fortunate groups or minorities.

Answer: TRUE Diff: 2 Page Ref: 151 23) Stream analytics is a term commonly used for the analytic process of extracting actionable information from large volumes of multi-structure data stored in organizational databases.

Answer: FALSE Diff: 2 Page Ref: 153 24) Descriptive statistics is all about drawing inferences or conclusions about the characteristics of the population using a sample of the underlying data.

Answer: FALSE Diff: 2 Page Ref: 158 25) Logistic regression is a very popular, statistically sound, probability-based classification algorithm that employs supervised learning.

Answer: TRUE Diff: 2 Page Ref: 173 26) Which characteristic of data means that all the required data elements are included in the data set?

A) Data source reliability B) Data accessibility C) Data richness D) Data granularity Answer: C Diff: 2 Page Ref: 123 27) Which of the following is not among the characteristics (metrics) that define the readiness level of data for an analytics study?

A) Data source reliability B) Data believability C) Data richness D) Data granularity Answer: B Diff: 3 Page Ref: 123-124

3 .

28) Which of the following is not among the characteristics (metrics) that define the readiness level of data for an analytics study?

A) Data security B) Data granularity C) Data currency D) Data transformation Answer: D Diff: 2 Page Ref: 123-124 29) Which of the following is among the characteristics (metrics) that define the readiness level of data for an analytics study?

A) Data acquisition B) Data aggregation C) Data cleaning D) Data relevancy Answer: D Diff: 2 Page Ref: 123-124 30) Unstructured data includes all of the following data types, except?

A) Textual data B) Video data C) Audio data D) Ordinal data Answer: D Diff: 2 Page Ref: 125-126 31) Which of the following is not an example for structured data?

A) State labels B) Weight of the vehicles C) Portrait picture of individuals D) Age of the people Answer: C Diff: 2 Page Ref: 125-126 32) Which of the following is not a true statement?

A) Nominal data is a type of structured data B) Textual data can be created from audio data C) Both ratio and interval data have absolute zero reference D) Multimedia data includes audio, video, and imagery data Answer: C Diff: 2 Page Ref: 125

4 .

33) Which of the following is an example for nominal data?

A) Customer review left on an e-commerce site B) Names of the states from where the customer orders are placed C) Maximum temperature recordings of the last five years D) Clickstream data obtained from a public website Answer: B Diff: 3 Page Ref: 126 34) Which of the following is among the reasons for data preprocessing?

A) Data in its original form is the most useful for analytics tasks B) Raw data is usually dirty, misaligned, and inaccurate C) Real-world data always comes in unstructured forma that needs to be structured D) The raw data that comes in non-numeric format needs to be converted to numbers Answer: B Diff: 2 Page Ref: 128 35) All of the following are among the major steps in data preprocessing, except?

A) Data reduction B) Data transformation C) Data consolidation D) Data security Answer: D Diff: 2 Page Ref: 128 36) Data reduction includes all of the following preprocessing tasks, except?

A) Reduce data dimensionality B) Reduce noise in data C) Balance the data D) Reduce data volume Answer: B Diff: 3 Page Ref: 128 37) Data consolidation includes all of the following preprocessing tasks, except?

A) Normalize data B) Select data C) Integrate data D) Collect data Answer: B Diff: 2 Page Ref: 128 38) Which of the following is not among the tasks performed for data transformation?

A) Create/derive new variables B) Sanitize the data C) Discretize the data D) Normalize the data Answer: B Diff: 2 Page Ref: 128 5 .

39) Which of the following is not a method used in dimensional reduction?

A) Use the findings of the previously published studies B) Run appropriate statistical tests C) Use normalized numerical variables D) Consult to domain experts' knowledge/opinion Answer: C Diff: 2 Page Ref: 129 40) The very first phase/step in data preprocessing is ________.

A) Data reduction B) Data transformation C) Data consolidation D) Data cleaning Answer: C Diff: 2 Page Ref: 128 41) Which of the following is not true about big data?

A) Big data is not just big volumes of data, it is also about the wide variety of data B) Big data can include structured as well as unstructured data C) Big data comes from the transactional databases withing the organization D) Big data is created in high speed, which can make it hard to collect and process Answer: C Diff: 2 Page Ref: 136-137 42) Where does Big Data come from?

A) IoT devices B) Internet and intranet C) Business processes and their data repositories D) Everywhere Answer: D Diff: 2 Page Ref: 136-137 43) Which of the following is not among the words used to define big data?

A) Veracity B) Velocity C) Variability D) Vulnerability Answer: D Diff: 2 Page Ref: 137

6 .

44) ________ is a big data characteristic that refers to conformity to facts: accuracy, quality, truthfulness, or trustworthiness of the data.

A) Veracity B) Velocity C) Variability D) Value Answer: A Diff: 2 Page Ref: 137 45) Which of the following is not among the reasons to seriously consider embarking on a Big Data journey?

A) You can't process the amount of data that you want to because of the limitations posed by your current platform or environment.

B) You want to involve new/contemporary data sources (e.g., social media, text, image, etc.) into your analytics platform, but you can't because it does not comply with the data storage schema.

C) You want to follow the data analytics processes taken by the leading companies in your global and highly competitive marketplace.

D) You need to and/or you want to integrate data as quickly as possible to be current on your analysis.

Answer: C Diff: 3 Page Ref: 140-141 46) Which of the following is not among the critical success factors for Big Data analytics adoption?

A) Intuition and experience based decision-making culture B) A clear business need that aligns with the vision and the strategy C) Alignment between the business and IT strategy D) Strong data infrastructure Answer: A Diff: 3 Page Ref: 141 47) To create a fact-based decision-making culture, senior management needs to do all of the following, except?

A) Recognize that some people can't or won't adjust B) Be a vocal supporter of fact-based decision making C) Stress that outdated methods must be discontinued D) Rely on the experiences and intuitions of the senior personnel Answer: D Diff: 3 Page Ref: 142 48) In big data analytics, high performance computing includes all of the following, except?

A) In-memory analytics B) In-database analytics C) Grid computing D) Business process reengineering Answer: D Diff: 2 Page Ref: 142 7 .

49) Challenges of big data includes all of the following, except?

A) Lack of structured data B) Data governance C) Processing capabilities D) Solution cost Answer: A Diff: 1 Page Ref: 142-143 50) ________ brings together hardware and software in a physical unit that is not only fast but also scalable on an as-needed basis.

A) Machine learning B) Appliances C) Data lakes D) Hadoop Answer: B Diff: 2 Page Ref: 142 51) Business problems addressed by big data analytics include all of the following, except?

A) Identifying new products and market opportunities B) Risk management C) Regulatory compliance D) All of the other answers are true Answer: D Diff: 1 Page Ref: 142 52) Which of the following is not among the big data technologies?

A) RDBMS B) HDFS C) MapReduce D) Hadoop Answer: A Diff: 2 Page Ref: 144 53) ________ is an open-source framework for processing, storing, and analyzing massive amounts of distributed, unstructured data.

A) NoSQL B) HDFS C) MapReduce D) Hadoop Answer: D Diff: 2 Page Ref: 144

8 .

54) Which of the following is the big data technique that distributes the processing of very large multi-structured data files across a large cluster of machines.

A) NoSQL B) HDFS C) MapReduce D) Hadoop Answer: C Diff: 2 Page Ref: 144 55) Which of the following is not one of the Hadoop technical components?

A) MapReduce B) Job Tracker C) Worker Nodes D) HDFS Answer: A Diff: 2 Page Ref: 144 56) ________ is a Hadoop-based data warehousing-like framework that allows users to write queries in an SQL-like language.

A) HBase B) Flume C) Hive D) HDFS Answer: C Diff: 3 Page Ref: 147 57) In big data analytics, ________ is a workflow processing system that lets users define a series of jobs written in multiple languages and then intelligently link them to one another.

A) HBase B) Flume C) Hive D) Oozie Answer: D Diff: 3 Page Ref: 147 58) Which of the following is not among the facts about Hadoop?

A) Hadoop is open source but available from vendors B) Hadoop can effectively replace traditional data warehouses C) Hadoop is an ecosystem, not a single product D) Hadoop is about data diversity, not just data volume Answer: B Diff: 2 Page Ref: 147

9 .

59) ________ is an open-source Big Data processing system developed to address the challenges of Big Data, especially the efficiency related challenges.

A) Deep learning B) Spark C) MapReduce D) Appliances Answer: B Diff: 2 Page Ref: 149 60) The benefits of Hadoop include all of the following, except?

A) Handling the Big Data with commodity hardware B) Significantly faster processing of big data for data analytics C) Scaling from a small cluster to a very large analytics system D) Enabling the discovery of knowledge from Big Data cost effectively Answer: B Diff: 3 Page Ref: 149 61) The most commonly cited benefits of Spark include all of the following, except?

A) Order of magnitude faster processing of big data B) A unified engine that supports highly efficient SQL queries, streaming data, machine learning and graph processing C) Preventing loss of data and information due to hardware failures through replications D) A revamped APIs designed for ease of use, especially for processing of unstructured and semi-structured data Answer: C Diff: 3 Page Ref: 150 62) In which of the following metric, Spark is better than Hadoop?

A) Performance B) Cost C) Parallel processing D) Scalability Answer: A Diff: 2 Page Ref: 149-150 63) Which of the following is not among the reasons to use Spark, instead of Hadoop?

A) Dealing with chains of parallel operations by using iterative algorithms B) Achieving quick results with in-memory computations C) Analyzing stream data analysis in real time D) Processing big data sets in environments where data size exceeds available memory Answer: D Diff: 3 Page Ref: 151

10 .

64) Which of the following is not among the reasons to use Hadoop, instead of Spark?

A) Graph-parallel processing to model data B) Batch processing with tasks that exploit disk read and write operations C) Building data analysis infrastructure with a limited budget D) Completing jobs that are not time-sensitive Answer: A Diff: 3 Page Ref: 151 65) ________ is a term commonly used for the analytic process of extracting actionable information from continuously flowing/streaming data.

A) Data analytics B) Social network analysis C) Stream analytics D) Machine learning Answer: C Diff: 2 Page Ref: 149-150 66) ________ is a collection of mathematical techniques to characterize and interpret data, which has been around for a very long time.

A) Operational research B) Machine learning C) Statistics D) Computational linguistics Answer: C Diff: 2 Page Ref: 149-157 67) ________ is a type of statistics that is used to draw inferences or conclusions about the characteristics of the population using a sample of the data.

A) Predictive statistics B) Descriptive statistics C) Prescriptive statistics D) Inferential statistics Answer: D Diff: 2 Page Ref: 158 68) The measure of center value in a given data set is often called ________.

A) Mean B) Mode C) Median D) Inner quartile range Answer: C Diff: 2 Page Ref: 159

11 .

69) As a dispersion measure, ________ is the difference between the largest and the smallest values in a given data set.

A) Standard deviation B) Variance C) Range D) Mean absolute percent error Answer: C Diff: 1 Page Ref: 160 70) Which of the following is a true statement about linear and logistic regression?

A) Both linear and logistic regression can use numerical as well as non-numeric input variables B) Linear regression predicts a non-numerical output variable C) Linear regression can have only one input variable, but many output variables D) Logistic regression is a classification method Answer: D Diff: 1 Page Ref: 172-174 71) List and briefly explain the most important assumptions in linear regression.

Answer: 1. Linearity. This assumption states that the relationship between the response variable and the explanatory variables is linear. That is, the expected value of the response variable is a straightline function of each explanatory variable, while holding all other explanatory variables fixed.

Also, the slope of the line does not depend on the values of the other variables. It also implies that the effects of different explanatory variables on the expected value of the response variable are additive in nature.

2. Independence (of errors). This assumption states that the errors of the response variable are uncorrelated with each other. This independence of the errors is weaker than actual statistical independence, which is a stronger condition and is often not needed for linear regression analysis.

3. Normality (of errors). This assumption states that the errors of the response variable are normally distributed. That is, they are supposed to be totally random and should not represent any nonrandom patterns.

4. Constant variance (of errors). This assumption, also called homoscedasticity, states that the response variables have the same variance in their error, regardless of the values of the explanatory variables. In practice this assumption is invalid if the response variable varies over a wide enough range/scale.

5. Multicollinearity. This assumption states that the explanatory variables are not correlated (i.e., do not replicate the same but provide a different perspective of the information needed for the model). Multicollinearity can be triggered by having two or more perfectly correlated explanatory variables presented to the model (e.g., if the same explanatory variable is mistakenly included in the model twice, one with a slight transformation of the same variable). A correlation-based data assessment usually catches this error.

Diff: 2 Page Ref: 172-173

12 .

72) Define and compare linear and logistic regressions. What is the key difference between the two?

Answer: They both are predictive modeling techniques.

They both use numerical input variables.

The biggest difference is that Linear regression has a numerical target variable to predict while Logistic Regression has a categorial dependent variable to predict. That is, while linear regression is for estimation type prediction, logistic regression is used for classification type prediction problems.

Diff: 2 Page Ref: 173-174 73) What is time series forecasting? How does it relate to simple linear regression? Provide an example to time series prediction.

Answer: Time series forecasting is the use of mathematical modeling to predict future values of the variable of interest based on previously observed values.

Time series can have linear trend, seasonality, cycles, and random variation.

The identification of trend in time series forecasting is the same method used in simple linear regression.

Examples: Predicting the next day's stock closing price; predicting the max temperature for tomorrow, predicting the sales for the next quarter [by using the past several periods actual numbers].

Diff: 2 Page Ref: 179-180 74) Describe the difference between descriptive and inferential statistics.

Answer: The main difference between descriptive and inferential statistics is the data used in these methods–whereas descriptive statistics is all about describing the sample data on hand, and inferential statistics is about drawing inferences or conclusions about the characteristics of the population.

Diff: 2 Page Ref: 157-158

13 .

75) List and briefly define at least five of the most pronounced characteristics (metrics) that define the readiness level of data for an analytics study.

Answer: Data source reliability refers to the originality and appropriateness of the storage medium where the data is obtained–answering the question of "Do we have the right confidence and belief in this data source?"

Data content accuracy means that data are correct and are a good match for the analytics problem–answering the question of "Do we have the right data for the job?"

Data accessibility means that the data are easily and readily obtainable, answering the question of "Can we easily get to the data when we need to?"

Data security and data privacy means that the data is secured to only allow those people who have the authority and the need to access it and to prevent anyone else from reaching it.

Data richness means that all the required data elements are included in the data set. In essence, richness (or comprehensiveness) means that the available variables portray a rich enough dimensionality of the underlying subject matter for an accurate and worthy analytics study.

Data consistency means that the data are accurately collected and combined/ merged. Consistent data represent the dimensional information (variables of interest) coming from potentially disparate sources but pertaining to the same subject.

Data currency/data timeliness means that the data should be up to date (or as recent/new as it needs to be) for a given analytics model. It also means that the data is recorded at or near the time of the event or observation so that the time-delay-related misrepresentation (incorrectly remembering and encoding) of the data is prevented.

Data granularity requires that the variables and data values be defined at the lowest (or as low as required) level of detail for the intended use of the data.

Data validity is the term used to describe a match/mismatch between the actual and expected data values of a given variable.

Data relevancy means that the variables in the data set are all relevant to the study being conducted.

Diff: 2 Page Ref: 123 76) What are the different data types in analytics? List, briefly define, and exemplify each.

Answer: Structured -Numerical [interval, ratio] -Categorical [nominal, ordinal] Unstructured/Semi-structured -Textual [Word, pdf, text] -Multimedia [image, video, audio] -Web/Log [XML, JSON] Diff: 2 Page Ref: 125-126

14 .

77) List and define data preprocessing phases and sub-tasks.

Answer: Data Consolidation • Collect data • Select data • Integrate data Data Cleaning • Impute values • Reduce noise • Eliminate duplicates Data Transformation • Normalize data • Discretize data • Create attributes Data Reduction • Reduce dimension • Reduce volume • Balance data Diff: 2 Page Ref: 128 78) What is big data? List and define the six keywords that collectively define big data.

Answer: Big data is the modern-day data that comes in large volumes, multiple varieties, and at a very high speed. It is everything that can be digitized and stored in computer environment, from database to image, audio to machine generated logs.

Volume Variety Velocity Veracity Variability Value Diff: 2 Page Ref: 138-139 79) List and describe the critical success factors for big data analytics.

Answer: 1. A clear business need (alignment with the vision and the strategy).

2. Strong, committed sponsorship (executive champion) 3. Alignment between the business and IT strategy 4. A fact-based decision-making culture 5. A Strong data infrastructure 6. The right analytics tools 7. Personnel with advanced analytics skills Diff: 2 Page Ref: 141-142

15 .

80) What are the similarities and differences between Hadoop and Spark? Why should you use Spark instead of Hadoop?

Answer: They both are Big Data technologies developed by Apache Software Foundation.

Hadoop was released in 2004 and Spark was released in 2009; so Spark is newer, developed to handle drawback of Hadoop.

Use Spark for: • Dealing with chains of parallel operations by using iterative algorithms • Achieving quick results with in-memory computations • Analyzing stream data analysis in real time • Graph-parallel processing to model data • All ML applications Diff: 2 Page Ref: 149-151

16 .

Business Intelligence, Analytics, Data Science, and AI, 5e (Sharda) Chapter 4 Descriptive Analytics II: Business Intelligence Data Warehousing, and Visualization 1) Business intelligence systems rely on a data warehouse as the information source for creating insight and supporting managerial decisions.

Answer: TRUE Diff: 1 Page Ref: 189 2) The "islands of data" problem in the 1980s describes the phenomenon of fully integrated data being stored in a central location within an organization.

Answer: FALSE Diff: 1 Page Ref: 191 3) Subject oriented databases for data warehousing are organized by detailed subjects such as disk drives, computers, and networks.

Answer: FALSE Diff: 2 Page Ref: 193 4) Data warehouses are subsets of data marts.

Answer: FALSE Diff: 1 Page Ref: 194 5) One way an operational data store differs from a data warehouse is the recency of their data.

Answer: TRUE Diff: 2 Page Ref: 194 6) Organizations seldom devote a lot of effort to creating metadata because it is not important for the effective use of data warehouses.

Answer: FALSE Diff: 1 Page Ref: 195 7) Without middleware, different BI programs cannot easily connect to the data warehouse.

Answer: TRUE Diff: 2 Page Ref: 196 8) Two-tier data warehouse/BI infrastructures offer organizations more flexibility but cost more than three-tier ones.

Answer: FALSE Diff: 2 Page Ref: 197 9) Moving the data into a data warehouse is usually the easiest part of creating and sustaining a data warehousing system.

Answer: FALSE Diff: 2 Page Ref: 199

1 .

10) The hub-and-spoke data warehouse model uses a centralized warehouse feeding dependent data marts.

Answer: TRUE Diff: 2 Page Ref: 201 11) Because of performance and data quality issues, most experts agree that the federated architecture should supplement data warehouses, not replace them.

Answer: TRUE Diff: 2 Page Ref: 201 12) Bill Inmon advocates the data mart bus architecture whereas Ralph Kimball promotes the hub-and-spoke architecture, a data mart bus architecture with conformed dimensions.

Answer: FALSE Diff: 2 Page Ref: 202 13) Properly integrating data from various databases and other disparate sources is not a trivial process.

Answer: TRUE Diff: 1 Page Ref: 211 14) Enterprise information integration is an evolving tool space that promises real-time data integration from a variety of sources, such as relational databases, Web services, and multidimensional databases.

Answer: TRUE Diff: 2 Page Ref: 213 15) A report is any communication artifact prepared with the specific intention of conveying information in a digestible form to whoever needs it, whenever and wherever they may need it.

Answer: TRUE Diff: 1 Page Ref: 222 16) OLTP systems are designed to handle ad hoc analysis and complex queries that deal with many data items.

Answer: FALSE Diff: 2 Page Ref: 209 17) The snowflake schema is a logical arrangement of tables in a multidimensional database in such a way that the entity-relationship diagram resembles a star in shape.

Answer: FALSE Diff: 2 Page Ref: 208 18) User-initiated navigation of data through disaggregation is referred to as "drill up."

Answer: FALSE Diff: 3 Page Ref: 210

2 .

19) ETL in data warehousing stands for engaging, training, and leveraging of the organizational information sources.

Answer: FALSE Diff: 2 Page Ref: 213 20) Data lake is a large storage location that can hold vast quantities of structured data in relational/tabular format for potential consumption by analytics processes.

Answer: FALSE Diff: 3 Page Ref: 217 21) Cloud computing enables information systems applications to dynamically scale up as workloads increase.

Answer: TRUE Diff: 1 Page Ref: 217 22) While data warehousing processing is schema-on-read, for data lakes it is schema-on-write.

Answer: FALSE Diff: 2 Page Ref: 218 23) Compared to data lakes, retrieval process for data warehouses is significantly faster.

Answer: TRUE Diff: 2 Page Ref: 218 24) In-database processing refers to the integration of the algorithmic extent of data analytics into data warehouses.

Answer: TRUE Diff: 1 Page Ref: 221 25) Key performance indicators (KPI) are a set of metrics used to measure the efficiency of the computational infrastructure of a business intelligence system.

Answer: FALSE Diff: 2 Page Ref: 223 26) Which of the following is not among the key criteria in selecting an ETL tool?

A) Ability to read from and write to various data source architectures B) Automatic capturing and delivery of metadata C) A history of conforming to open standards D) Cost of acquisition of the tool Answer: D Diff: 3 Page Ref: 215

3 .

27) Operational or transaction databases are product oriented, handling transactions that update the database. In contrast, data warehouses are ________.

A) Subject-oriented and nonvolatile B) Product-oriented and nonvolatile C) Product-oriented and volatile D) Subject-oriented and volatile Answer: A Diff: 2 Page Ref: 193 28) Which kind of data warehouse is created separately from the enterprise data warehouse by a department and not reliant on it for updates?

A) Sectional data mart B) Public data mart C) Independent data mart D) Volatile data mart Answer: C Diff: 2 Page Ref: 194 29) In data warehousing, oper marts are created when operational data needs to be analyzed in which of the following format?

A) Linearly B) Unidirectionally C) Unidimensionally D) Multidimensionally Answer: D Diff: 2 Page Ref: 195 30) Which of the following best enables a data warehouse to handle complex queries and scale up to handle many more requests?

A) Use of the Web by users as a front-end B) Parallel processing C) Machine learning D) A larger IT staff Answer: B Diff: 2 Page Ref: 199 31) Which data warehouse architecture uses metadata from existing data warehouses to create a hybrid logical data warehouse comprised of data from the other warehouses?

A) Independent data marts architecture B) Centralized data warehouse architecture C) Hub-and-spoke data warehouse architecture D) Federated architecture Answer: D Diff: 3 Page Ref: 201

4 .

32) Which approach to data warehouse integration focuses more on sharing process functionality than data across systems?

A) Extraction, transformation, and load B) Enterprise application integration C) Enterprise information integration D) Enterprise function integration Answer: B Diff: 3 Page Ref: 213 33) In which stage of extraction, transformation, and load into a data warehouse are anomalies detected and corrected?

A) Transformation B) Extraction C) Load D) Cleanse Answer: D Diff: 3 Page Ref: 213 34) Data warehouses provide direct and indirect benefits to organizations. Which of the following is an indirect benefit of data warehouses?

A) Better and more timely information B) Extensive new analyses performed by users C) Simplified access to data D) Improved customer service Answer: D Diff: 3 Page Ref: 203 35) All of the following are benefits of hosted data warehouses, except?

A) Smaller upfront investment B) Better quality hardware C) Greater control of data D) Frees up in-house systems Answer: C Diff: 2 Page Ref: 207 36) When representing data in a data warehouse, using several dimension tables that are each connected only to a fact table means you are using which warehouse structure?

A) Star schema B) Snowflake schema C) Normalized relational schema D) Multi-dimensional schema Answer: A Diff: 3 Page Ref: 208

5 .

37) When querying a dimensional database, a user went from summarized data to its underlying details. The function that served this purpose is called ________.

A) Dice B) Slice C) Roll-up D) Drill down Answer: D Diff: 2 Page Ref: 210 38) ________ implies that the refresh cycle of an existing data warehouse to update the data is more frequent, almost instantaneous.

A) Data warehouse appliances B) Columnar data representation C) Real-time data warehousing D) In-database processing technology Answer: C Diff: 1 Page Ref: 220 39) A large storage location that can hold vast quantities of data (mostly unstructured) in its native/raw format for future/potential analytics consumption is referred to as a(n) ________.

A) Extended ASP B) Data cloud C) Data lake D) Relational database Answer: C Diff: 2 Page Ref: 217 40) How does the use of cloud computing affect the scalability of a data warehouse?

A) Cloud computing vendors bring as much hardware as needed to users' offices B) Hardware resources are dynamically allocated as usage increases C) Cloud vendors are mostly based overseas where the cost of labor is low D) Cloud computing has little effect on a data warehouse's scalability Answer: B Diff: 3 Page Ref: 217 41) All of the following are true about in-database processing technology, except?

A) It pushes the algorithms to where the data is B) It makes the response to queries much faster than conventional databases C) It is often used for apps like credit card fraud detection and investment risk management D) It is the same as in-memory storage technology Answer: D Diff: 3 Page Ref: 221

6 .

42) ________ is a report type that focuses on not only financial, but also customer, business process, and learning and growth perspectives.

A) Six Sigma B) Dashboard C) Balanced Scorecard D) KPI Answer: C Diff: 3 Page Ref: 224 43) Which of the following is not among the three main types of data warehouses?

A) Data marts (DMs) B) Operational data stores (ODS) C) Enterprise data warehouses (EDW) D) Virtual data warehouses (VDW) Answer: D Diff: 2 Page Ref: 194 44) Which of the following refers to the components in data warehousing that best describe the structure and meaning of the data, contributing to their effective use?

A) Transactional data B) Operational data C) Metadata D) Granular data Answer: C Diff: 2 Page Ref: 195 45) Most data warehouses are built using what type of database management systems to control and manage the data?

A) Transactional B) Relational C) Sequential D) Bidirectional Answer: B Diff: 2 Page Ref: 199 46) A ________ architecture is used to build a scalable and maintainable infrastructure that includes a centralized data warehouse and several dependent data marts.

A) Federated B) Data mart C) Centralized D) Hub-and-spoke Answer: D Diff: 2 Page Ref: 200-201

7 .

47) Which of the following data warehouse architecture involves integrating disparate systems and analytical resources from multiple sources to meet changing needs or business conditions?

A) Federated B) Data mart C) Centralized D) Hub-and-spoke Answer: A Diff: 3 Page Ref: 201 48) Data integration comprises all of the following, except?

A) Data transformation B) Data access C) Data federation D) Change capture Answer: A Diff: 2 Page Ref: 211 49) ________ is a mechanism that integrates application functionality and shares functionality (rather than data) across systems, thereby enabling flexibility and reuse.

A) Service-oriented architecture (SOA) B) Enterprise information integration (EII) C) Extraction, transformation, and load (ETL) D) Enterprise application integration (EAI) Answer: D Diff: 3 Page Ref: 213 50) ________ is a mechanism for pulling data from source systems to satisfy a request for information. It is an evolving tool space that promises real-time data integration from a variety of sources, such as relational databases, Web services, and multidimensional databases.

A) Service-oriented architecture (SOA) B) Enterprise information integration (EII) C) Extraction, transformation, and load (ETL) D) Enterprise application integration (EAI) Answer: B Diff: 3 Page Ref: 213 51) Performing extensive ________ to move data to the data warehouse may be a sign of poorly managed data and a fundamental lack of a coherent data management strategy.

A) System-level integration (SLI) B) Enterprise information integration (EII) C) Extraction, transformation, and load (ETL) D) Enterprise application integration (EAI) Answer: C Diff: 2 Page Ref: 215

8 .

52) The ________, also known as the EDW approach, emphasizes top-down development, employing established database development methodologies and tools, such as entityrelationship diagrams (ERD), and an adjustment of the spiral development approach.

A) Inmon model B) Integration model C) Kimball model D) Application model Answer: A Diff: 2 Page Ref: 204 53) Which of the following is the model that follows the ground-up, plan big build small, type data warehousing approach?

A) Inmon model B) Integration model C) Kimball model D) Application model Answer: C Diff: 2 Page Ref: 204 54) In representation of data in data warehouse, which of the following retrieval-based systems supports high-volume query access?

A) Relational modeling B) Dimensional modeling C) Transactional modeling D) Persistent modeling Answer: B Diff: 3 Page Ref: 208 55) Which of the following is a subset that is created directly from the data warehouse?

A) Dependent data mart B) Independent data mart C) Operational data store D) Federated data mart Answer: A Diff: 2 Page Ref: 199-200 56) ________ is a term used for a transaction system that is primarily responsible for capturing and storing data related to day-to-day business functions such as ERP, CRM, and SCM. A) OLAP B) OLTP C) Data warehouse D) Data mart Answer: B Diff: 2 Page Ref: 209

9 .

57) Given that the size of data warehouses is expanding at an exponential rate, which of the following is the most relevant issue that the organization must consider and deal with?

A) Security B) Privacy C) Accuracy D) Scalability Answer: D Diff: 2 Page Ref: 195 58) The role responsible for successful management of a data warehouse is often called which of the following?

A) Data warehouse scientist B) Data warehouse architect C) Data warehouse administrator D) Data warehouse designer Answer: C Diff: 1 Page Ref: 216 59) ________ is a creative way of deploying information system applications where the provider licenses its applications to customers for use as a service on demand (usually over the Internet).

A) Cloud computing B) Software as a service C) Open-source software D) Big data analytics Answer: B Diff: 2 Page Ref: 217 60) Which of the following is not true about data warehouses and data lakes?

A) Data lakes can handle any data in native format B) Data retrieval is faster in data warehousing C) Data lakes are for business users while data warehouses are for data scientists D) Data warehouses are less agile as compared to data lakes Answer: C Diff: 3 Page Ref: 218 61) ________ is a system that stores data tables as sections of columns of data rather than as rows of data (which is the way most RDBMS do it).

A) Star schema B) Snowflake schema C) Multi-dimensional modeling D) Columnar Answer: D Diff: 2 Page Ref: 219

10 .

62) Which of the following is the data warehouse technology that consists of an integrated set of servers, storage, operating system(s), database management systems, and software.

A) Data mart B) ETL C) Appliance D) Columnar Answer: C Diff: 2 Page Ref: 220 63) ________ is any communication artifact prepared with the specific intention of conveying information in a digestible form to whoever needs it, whenever and wherever they may need it.

A) Data warehouse B) Data mart C) Report D) Relational database system Answer: C Diff: 2 Page Ref: 222 64) Which of the following is not among the functions filled by business report?

A) To ensure optimal decision making B) To provide information C) To provide the results of an analysis D) To create an organizational memory Answer: A Diff: 2 Page Ref: 222 65) In performance management of an organization, ________ are enterprise-wide agreed targets to be tracked against over a period of time.

A) ERP B) SCM C) KPI D) ETL Answer: C Diff: 2 Page Ref: 223 66) Which of the following is not among the basic charts and graphs?

A) Pie chart B) Bar chart C) Gantt chart D) Bubble chart Answer: C Diff: 2 Page Ref: 227-228

11 .

67) ________ is the chart type that shows precedence relationships among the project activities/tasks and is used for planning and scheduling of large and complex projects.

A) Scatter B) PERT C) Bubble D) Line Answer: B Diff: 2 Page Ref: 228 68) Which of the following is not among the best practices in story telling with data?

A) Think of your analysis as a story B) Be authentic C) Be visual, think of yourself as a film editor D) Make it convoluted for your audience to better draw their attention Answer: D Diff: 2 Page Ref: 235 69) ________ is the combination of visualization and predictive analytics.

A) Data warehousing B) Visual analytics C) Statistics D) Data visualization Answer: B Diff: 2 Page Ref: 235 70) Which of the following is the type of visual usually used to show the frequency distribution of a numerical variable?

A) Line chart B) Heat map C) Histogram D) Scatter plot Answer: C Diff: 2 Page Ref: 228 71) What is the definition of a data warehouse (DW) in simple terms? How does it differ from transactional databases?

Answer: In simple terms, a data warehouse (DW) is a pool of data produced to support decision making; it is also a repository of current and historical data of potential interest to managers throughout the organization.

Data warehouse is for decision making while transactional databases are for business process execution. Both use RDBMS, however, while transactional databases are normalized, data warehouses are denormalized for query efficiency purposes.

Diff: 2 Page Ref: 190

12 .

72) A common way of introducing data warehousing is to refer to its fundamental characteristics.

List and describe at least five characteristics of data warehousing.

Answer: • Subject oriented. Data are organized by detailed subjects, such as sales, products, or customers, containing only information relevant for decision support.

• Integrated. Integration is closely related to subject orientation. Data warehouses must place data from different sources into a consistent format. To do so, they must deal with naming conflicts and discrepancies among units of measure. A data warehouse is presumed to be totally integrated.

• Time variant (time series). A warehouse maintains historical data. The data do not necessarily provide current status (except in real-time systems). They detect trends, deviations, and longterm relationships for forecasting and comparisons, leading to decision making. Every data warehouse has a temporal quality. Time is the one important dimension that all data warehouses must support. Data for analysis from multiple sources contains multiple time points (e.g., daily, weekly, monthly views).

• Nonvolatile. After data are entered into a data warehouse, users cannot change or update the data. Obsolete data are discarded, and changes are recorded as new data.

• Web based. Data warehouses are typically designed to provide an efficient computing environment for Web-based applications.

• Relational/multidimensional. A data warehouse uses either a relational structure or a multidimensional structure.

• Client/server. A data warehouse uses the client/server architecture to provide easy access for end users.

• Real time. Newer data warehouses provide real-time, or active, data-access and analysis capabilities (see Basu, 2003; and Bonde and Kuckuk, 2004).

• Include metadata. A data warehouse contains metadata (data about data) about how the data are organized and how to effectively use them.

Diff: 3 Page Ref: 193-194 73) What is the definition of a data mart? What are the three reasons one would choose a data mart over a data warehouse?

Answer: A data mart is a subset of a data warehouse, typically consisting of a single subject area (e.g., marketing, operations). Whereas a data warehouse combines databases across an entire enterprise, a data mart is usually smaller and focuses on a particular subject or department.

Reasons: cost, efficiency (take significantly less time to implement), speed (focus on a single department/function data, hence faster in responding to complex queries).

Diff: 2 Page Ref: 194-195 74) Describe the data warehousing framework, from data ingestion to knowledge dissemination.

Make sure to name and describe the major layers and enabling components.

Answer: Data warehousing starts with acquisition of all kinds of data (ERP, Legacy, POS, Web, and external data) via a process called ETL (extract, transform, cleanse, and load). The data is stored in an EDW, and replicated, and often this EDW is used to create data marts for efficiency. The EDW also maintains metadata. Then a middleware layer is used as the connection between data marts and the end user, so that the end user can access the desired information, in desired format, on an as needed basis.

Diff: 2 Page Ref: 196-197 13 .

75) List and briefly describe the five types of data warehouse architectures.

Answer: 1. Independent data marts. This is arguably the simplest and the least costly architecture alternative. The DMs are developed to operate independent of each other to serve the needs of individual organizational units.

2. Data mart bus architecture. This architecture is a viable alternative to the independent DMs where the individual marts are linked to each other via some kind of middleware.

3. Hub-and-spoke architecture. This is perhaps the most famous data warehousing architecture today. Here the attention is focused on building a scalable and maintainable infrastructure (often developed in an iterative way, subject area by subject area) that includes a centralized data warehouse and several dependent DMs (each for an organizational unit).

4. Centralized data warehouse. The centralized data warehouse architecture is similar to the hub-and-spoke architecture except that there are no dependent DMs; instead, there is a gigantic EDW that serves the needs of all organizational units.

5. Federated data warehouse. The federated approach is a concession to the natural forces that undermine the best plans for developing a perfect system. It uses all possible means to integrate analytical resources from multiple sources virtually to meet changing needs or business conditions.

Diff: 3 Page Ref: 200-201 76) Describe the two dominant data warehouse development approaches proposed by Inman and Kimball. Compare the two approaches in terms of scope, development time, cost, and size.

Answer: Inmon's approach emphasizes top-down development, employing established database development methodologies and tools, such as entity-relationship diagrams (ERD) and an adjustment of the spiral development approach.

Kimball's DM strategy is a "plan big, build small" approach. A DM is a subject-oriented or department-oriented data warehouse. It is a scaled-down version of a data warehouse.

Kimball Scope One subject area Development time Short/months Cost Low Size Small Diff: 3 Page Ref: 204-206

Inmon Broad, enterprise Long/years High Large

14 .

77) Define and compare OLAP and OLTP. List and briefly define the five OLAP operations.

Answer: OLTP (online transaction processing system) is a term used for a transaction system that is primarily responsible for capturing and storing data related to day-to-day business functions such as ERP, CRM, SCM, POS, and so forth.

OLAP, on the other hand, is designed to address the information (i.e., decision support) needs in the organization by providing ad hoc analysis of organizational data effectively and efficiently.

OLAP and OLTP rely heavily on each other: OLAP uses the data captured by OLTP, and OLTP automates the business processes that are managed by decisions supported by OLAP. OLAP Operations include: 1. Slice. A slice is a subset of a multidimensional array (usually a two-dimensional representation) corresponding to a single value set for one (or more) of the dimensions not in the subset.

2. Dice. The dice operation is a slice on more than two dimensions of a data cube.

3. Drill Down/Up. Drilling down or up is a specific OLAP technique whereby the user navigates among levels of data ranging from the most summarized (up) to the most detailed (down).

4. Roll-up. A roll-up involves computing all the data relationships for one or more dimensions.

To do this, a computational relationship or formula might be defined.

5. Pivot. This is used to change the dimensional orientation of a report or ad hoc query-page display.

Diff: 3 Page Ref: 209-210 78) What is ETL in data warehousing? List and briefly describe its process.

Answer: ETL stands for extract, transform, load operations in DW. The ETL process consists of extraction (i.e., reading data from one or more databases), transformation (i.e., converting the extracted data from its previous form into the form in which it needs to be so that it can be placed into a data warehouse or simply another database), and load (i.e., putting the data into the data warehouse).

Diff: 2 Page Ref: 214

15 .

79) Define visual analytics. List and briefly describe three basic and three specialized charts and graphs.

Answer: Visual analytics is the combination of visualization and predictive analytics.

Basic Charts/Graphs • Line chart • Bar chart • Pie chart • Scatter plot • Bubble chart Specialized charts/graphs • Histogram • Gantt • PERT • Geographic Map • Bullet • Heat map • Highlight table • Tree map Diff: 2 Page Ref: 233, 227-229 80) Briefly describe at least four of the recently popularized infrastructure concepts and technologies that will play a significant role in defining the future of data warehousing.

Answer: Infrastructure (architectural–hardware and software–enhancements) concept and technologies include: • Columnar (a new way to store and access data in the database) • Real-time data warehousing • Data warehouse appliances (all-in-one solutions to DW) • Data management technologies and practices • In-database processing technology (putting the algorithms where the data is) • In-memory storage technology (moving the data in the memory for faster processing) • New database management systems • Advanced analytics Diff: 2 Page Ref: 219-220

16 .

Business Intelligence, Analytics, Data Science, and AI, 5e (Sharda) Chapter 5 Predictive Analytics I: Data Mining Process, Methods, and Algorithms 1) Data mining is a way to develop intelligence (i.e., actionable information or knowledge) from textual data that an organization collects, organizes, and stores.

Answer: FALSE Diff: 2 Page Ref: 249 2) The cost of data storage has plummeted recently, initiating the amount of data stored in electronic form grown at an explosive rate, making data mining feasible for more firms.

Answer: TRUE Diff: 2 Page Ref: 254 3) Data mining can be very useful in detecting patterns such as credit card fraud but is of little help in improving sales.

Answer: FALSE Diff: 1 Page Ref: 254 4) If using a mining analogy, "knowledge mining" would be a more appropriate term than "data mining."

Answer: TRUE Diff: 2 Page Ref: 256 5) Consolidation of databases and other data repositories into a single location in the form of a data warehouse had no impact on increased popularity of data mining.

Answer: FALSE Diff: 1 Page Ref: 254 6) Data mining requires specialized data analysts to ask ad hoc questions and obtain descriptive answers quickly from the varied data system.

Answer: FALSE Diff: 2 Page Ref: 257 7) Confusion matrix can only be used for binary classification problems where there are two outcomes, such as true and false for the target variable.

Answer: FALSE Diff: 1 Page Ref: 273 8) The value provided by area under the curve (AUC) ranges between -1 and 1 and is the true representation of the prediction accuracy.

Answer: FALSE Diff: 2 Page Ref: 276 9) Bayesian classifiers use probability theory to build classification models based on past occurrences that are capable of placing a new instance into a most probable class.

Answer: TRUE Diff: 2 Page Ref: 276 1 .

10) In data mining, classification models help in prediction of cases that belong to two or more categories.

Answer: TRUE Diff: 2 Page Ref: 258 11) Statistics and data mining both look for data sets that are as large and as varied as possible.

Answer: FALSE Diff: 2 Page Ref: 261 12) In CRISP-DM, testing and evaluation step is not focused on evaluating the accuracy of different models, rather it is about assessing whether the business problem is appropriately addressed.

Answer: TRUE Diff: 1 Page Ref: 269 13) In KDD standardized process, data mining encompasses the whole process, not just one step.

Answer: FALSE Diff: 2 Page Ref: 271 14) During classification in data mining, a false positive is an occurrence classified as positive by the algorithm while being negative in reality.

Answer: TRUE Diff: 1 Page Ref: 272 15) k-fold cross-validation methodology can also be called rotation estimation.

Answer: TRUE Diff: 2 Page Ref: 275 16) Bootstrapping is like the k-fold cross-validation where the k takes the value of 1.

Answer: FALSE Diff: 3 Page Ref: 275 17) The area under the ROC curve is a graphical assessment technique where the true positive rate is plotted on the y-axis and the false negative rate is plotted on the x-axis.

Answer: FALSE Diff: 3 Page Ref: 275 18) For classification type predictive modeling, confusion matrix is the primary source for accuracy estimation.

Answer: TRUE Diff: 2 Page Ref: 273 19) In predictive model assessment, the model's ability to make reasonably accurate predictions, given noisy data or data with missing and erroneous values is called scalability.

Answer: FALSE Diff: 2 Page Ref: 273 2 .

20) Data that is collected, stored, and analyzed in data mining is often private and personal. This type of data needs to go through a process of de-identification prior to applying data mining.

Answer: TRUE Diff: 2 Page Ref: 292 21) Data mining provides instant, crystal-ball-like insights is nothing but a myth.

Answer: TRUE Diff: 2 Page Ref: 294 22) K-means is the most popular algorithm used for association rule mining.

Answer: FALSE Diff: 2 Page Ref: 282-283 23) In data mining, clustering is an exploratory unsupervised data analysis tool for solving classification problems.

Answer: TRUE Diff: 2 Page Ref: 280 24) The main idea in market basket analysis is to predict future customer demand patterns so that an appropriate volume of merchandise would be produced or purchased.

Answer: FALSE Diff: 2 Page Ref: 283 25) K in K-means clustering refers to the dimensionality of the input data (i.e., the number of input variables) used in the analysis.

Answer: FALSE Diff: 2 Page Ref: 282 26) Which of the following is not among the key reasons data mining is gaining attention in the business world?

A) More intense competition at the global scale B) Significant increase in mergers and acquisition in the marketplace C) Recognition of the untapped value hidden in large data sources D) The exponential increase in data processing and storage technologies Answer: B Diff: 2 Page Ref: 254 27) Amazon and others have used analytics to do all of the following, except?

A) Collect data about customers and competitors B) Better understand their customers C) Maximize their returns on investment D) Optimize their extended supply chains Answer: A Diff: 3 Page Ref: 254

3 .

28) All of the following statements about data mining are true, except?

A) The process aspect means that data mining should be a one-step analytics task B) The novel aspect means that previously unknown patterns are discovered C) The potentially useful aspect means that results should lead to some business benefit D) The valid aspect means that the discovered patterns should hold true on new data Answer: A Diff: 2 Page Ref: 256 29) What is the main reason parallel processing is sometimes used for data mining?

A) Because the hardware exists in most organizations, and it is available to use B) Because most of the algorithms used for data mining require it C) Because of the massive data amounts and computational efforts involved D) Because any strategic application requires parallel processing Answer: C Diff: 3 Page Ref: 257 30) Which of the following is not among the alternative names used for data mining?

A) Knowledge extraction B) Pattern analysis C) Data archaeology D) Data browsing Answer: D Diff: 2 Page Ref: 256 31) Which of the following is not among the characteristics associated with the definition of data mining?

A) It is a trivial process B) It seeks novel patterns C) Findings are expected to be potentially useful D) Findings are expected to be ultimately understandable Answer: A Diff: 2 Page Ref: 256 32) Which broad area of data mining methods analyzes data, learns patterns from past data to place new instances into defined classes?

A) Associations B) Visualization C) Classification D) Clustering Answer: C Diff: 2 Page Ref: 272

4 .

33) Which broad area of data mining methods partitions a collection of objects into natural groupings with similar features?

A) Associations B) Visualization C) Classification D) Clustering Answer: D Diff: 2 Page Ref: 280 34) Clustering partitions a collection of things into segments whose members share which of the following?

A) Similar characteristics B) Similar variables C) Similar collection methods D) Similar volume of data Answer: A Diff: 2 Page Ref: 280 35) Which of the following is not among the factors used to assess a classification model?

A) Volume B) Speed C) Scalability D) Accuracy Answer: A Diff: 2 Page Ref: 272-273 36) All of the following statements about data mining are true, except?

A) The term is relatively new B) Its techniques have their roots in traditional statistical analysis and artificial intelligence C) The ideas behind it are relatively new D) Intense, global competition make its application more important Answer: C Diff: 3 Page Ref: 254 37) Which of the following data mining processes/methodologies is the most comprehensive?

A) SEMMA B) Six Sigma C) KDD Process D) CRISP-DM Answer: D Diff: 1 Page Ref: 264-274

5 .

38) Prediction problems where the dependent variables have numeric values are most properly defined as ________.

A) Classifications B) Regressions C) Associations D) Clustering Answer: B Diff: 2 Page Ref: 272 39) What does the robustness of a data mining method refer to?

A) Its ability to predict the outcome of a previously unknown data set accurately B) Its speed of computation and computational costs in using the model C) Its ability to construct a prediction model efficiently given a large amount of data D) Its ability to overcome noisy data to make accurate predictions Answer: D Diff: 2 Page Ref: 273 40) What does the scalability of a data mining method refer to?

A) Its ability to predict the outcome of a previously unknown data set accurately B) Its speed of computation and computational costs in using the mode C) Its ability to construct a prediction model efficiently given a large amount of data D) Its ability to overcome noisy data to make somewhat accurate predictions Answer: C Diff: 2 Page Ref: 273 41) In estimating the accuracy of classification models, the true positive rate is ________.

A) The ratio of correctly classified positives divided by the total positive count B) The ratio of correctly classified positives divided by the total negative count C) The ratio of correctly classified positives divided by the sum of correctly classified positives and incorrectly classified positives D) The ratio of correctly classified positives divided by the sum of correctly classified positives and correctly classified negatives Answer: A Diff: 2 Page Ref: 274 42) In data mining, finding products that frequently put into a shopping cart is known as ________.

A) Association rule mining B) Cluster analysis C) Decision trees D) Artificial neural networks Answer: A Diff: 1 Page Ref: 282-283

6 .

43) Third-party providers of publicly available data sets protect the anonymity of the individuals in the data set primarily by ________.

A) Asking data users to use the data ethically B) Leaving in identifiers (e.g., name), but changing other related variable values C) Removing identifiers such as names and social security numbers D) Letting individuals in the data know their data is being accessed Answer: C Diff: 3 Page Ref: 292 44) In the Target case study, why did Target send a teen maternity ads?

A) Target's analytic model confused her with an older woman with a similar name B) Target was sending ads to all women in a particular neighborhood C) Target's analytic model suggested she was pregnant based on her buying habits D) Target was using a special promotion that targeted all teens in her geographical area Answer: C Diff: 2 Page Ref: 293 45) Which of the following is a data mining myth?

A) Data mining is a multistep process that requires deliberate, proactive design and use B) Data mining requires a separate, dedicated database C) The current state-of-the-art is ready to go for almost any business D) Newer Web-based tools enable managers of all educational levels to do data mining Answer: B Diff: 2 Page Ref: 294 46) Which of the following is a data mining reality?

A) Data mining is not yet viable for mainstream business applications B) Only those with advanced degrees can do data mining.

C) Data mining provides instant, crystal-ball-like predictions D) If the data accurately reflect the business or its customers, any company can use data mining Answer: D Diff: 2 Page Ref: 294 47) Which of the following is not among the association type tasks in data mining?

A) Sequence analysis B) Time series analysis C) Link analysis D) Market-basket analysis Answer: B Diff: 2 Page Ref: 259

7 .

48) Knowledge extraction, pattern analysis, data archaeology, information harvesting, pattern searching, and data dredging are all alternative names for ________.

A) Data science B) Predictive modeling C) Data mining D) Market-basket analysis Answer: C Diff: 1 Page Ref: 256 49) Which of the following is not among the disciplines that underlies data mining?

A) Database management and data warehousing B) Statistics C) Information visualization D) Computational linguistic Answer: D Diff: 2 Page Ref: 257 50) ________ was proposed by a consortium of companies to serve as a nonproprietary standard methodology for data mining.

A) KDD B) SEMMA C) CRISP-DM D) Six Sigma Answer: C Diff: 1 Page Ref: 264 51) Which of the following is not among the prediction-type tasks in data mining?

A) Regression B) Time series analysis C) Link analysis D) Classification Answer: C Diff: 2 Page Ref: 259 52) Which of the following data mining methods does not use supervised learning?

A) Regression B) Time series analysis C) Link analysis D) Classification Answer: C Diff: 2 Page Ref: 259

8 .

53) It is usually believed that while prediction is largely experience and opinion based, ________ is data and model based.

A) Association B) Clustering C) Link analysis D) Forecasting Answer: D Diff: 3 Page Ref: 258 54) Whereas ________ starts with a well-defined proposition and hypothesis, data mining starts with a loosely defined discovery statement.

A) Association B) Clustering C) Statistics D) Forecasting Answer: C Diff: 2 Page Ref: 258 55) All of the following are data mining methods that use unsupervised learning, except?

A) Clustering B) Time series analysis C) Link analysis D) Market-basket analysis Answer: B Diff: 2 Page Ref: 259 56) Neural networks are used for which of the following data mining method?

A) Classification B) Regression C) Both classification and regression D) Association rule mining Answer: C Diff: 2 Page Ref: 259 57) Which of the following is not true about CRISP-DM process?

A) Even though steps in CRISP-DM are sequential, there is usually a great deal of backtracking B) It is the most popular standard process for data mining C) The process is composed of six consecutive steps D) The process is developed and popularized by SAS, one of the largest analytics companies Answer: D Diff: 1 Page Ref: 264

9 .

58) Which of the following statements is true about regression and classification?

A) Classification is a prediction method while regression is not a prediction method B) Regression is based on non-numerical data input, whereas classification is all numerical C) Both regression and classification use unsupervised learning D) While classification has a categorial, regression has a numerical target variable Answer: D Diff: 2 Page Ref: 272 59) Which of the following is not among the key factors considered in assessing classification type prediction models?

A) Prediction accuracy B) Concurrency C) Speed D) Interpretability Answer: B Diff: 2 Page Ref: 273 60) In ________, the complete data set is randomly split into more than two mutually exclusive subsets of approximately equal size and trained and tested multiple times.

A) Area under the curve B) Concurrency C) K-fold cross validation D) Single split Answer: C Diff: 2 Page Ref: 275 61) The basic idea behind a(n) ________ is that it recursively divides a training set until each division consists primarily of examples from one class.

A) Decision tree modeling B) Association rule mining C) K-fold cross validation D) Stratified random sampling Answer: A Diff: 2 Page Ref: 278-279 62) Which of the following is not among the well-known algorithms used for creating decision trees?

A) CART B) CHAID C) K-means D) ID3 Answer: C Diff: 2 Page Ref: 279

10 .

63) Because of its successful application to retail business problems, association rule mining is commonly called ________.

A) Customer relationship analysis B) Market-basket analysis C) Cluster analysis D) Customer analytics Answer: B Diff: 1 Page Ref: 282-284 64) The ________ is the most commonly used algorithm to discover association rules. Given a set of items, the algorithm attempts to find subsets that are common to at least a minimum number of the item sets.

A) K-means B) Market-basket analysis C) Apriori D) Gini index Answer: C Diff: 2 Page Ref: 284 65) Which of the following is not among the methods used for cluster analysis?

A) Genetic algorithms B) Neural networks C) K-means D) SVM Answer: D Diff: 2 Page Ref: 281 66) The process of intelligently combining the information (i.e., predictions) created by two or more information sources (i.e., prediction models) is usually called ________.

A) Neural network modeling B) Association rule mining C) Support vector machines D) Ensemble modeling Answer: D Diff: 2 Page Ref: 277 67) All of the following are among the known classification techniques, except?

A) Decision trees B) Neural networks C) Linear regression D) SVM Answer: C Diff: 2 Page Ref: 281

11 .

68) Which of the following is a well-known prediction technique?

A) Association rule mining B) Apriori C) K-means D) SVM Answer: D Diff: 2 Page Ref: 281 69) Which of the following is not true about accuracy metrics for classification methods?

A) Confusion matrix is the primary source for accuracy estimation B) True positive rate is also called sensitivity C) Recall is calculated by dividing true positive count into the sum of true positive and false negative counts D) Precision is also called specificity Answer: D Diff: 2 Page Ref: 274 70) All of the following are among the metrics used to assess the association rules, except?

A) Consistency B) Support C) Lift D) Confidence Answer: A Diff: 2 Page Ref: 283-284 71) List and briefly describe five reasons for the growing popularity of data mining in the business world.

Answer: 1) More intense competition at the global scale driven by customers' ever-changing needs and wants in an increasingly saturated marketplace 2) General recognition of the untapped value hidden in large data sources 3) Consolidation and integration of database records, which enables a single view of customers, vendors, transactions, etc.

4) Consolidation of databases and other data repositories into a single location in the form of a data warehouse 5) The exponential increase in data processing and storage technologies 6) Significant reduction in the cost of hardware and software for data storage and processing 7) Movement toward the demassification (conversion of information resources into nonphysical form) of business practices Diff: 2 Page Ref: 254

12 .

72) List four common data mining myths and corresponding realities.

Answer: 1) Myth: Data mining provides instant, crystal-ball-like predictions.

Reality: Data mining is a multistep process that requires deliberate, proactive design and use.

2) Myth: Data mining is not yet viable for mainstream business applications.

Reality: The current state of the art is ready to go for almost any business type and/or size.

3) Myth: Data mining requires a separate, dedicated database.

Reality: Because of the advances in database technology, a dedicated database is not required.

4) Myth: Only those with advanced degrees can do data mining.

Reality: Newer Web-based tools enable managers of all educational levels to do data mining.

5) Myth: Data mining is only for large firms that have lots of customer data.

Reality: If the data accurately reflect the business or its customers, any company can use data mining.

Diff: 2 Page Ref: 294 73) List and briefly describe the six steps of the CRISP-DM data mining process. Which steps are the most time demanding?

Answer: Step 1: Business Understanding – The key element of any data mining study is to know what the study is for. Answering such a question begins with a thorough understanding of the managerial need for new knowledge and an explicit specification of the business objective regarding the study to be conducted.

Step 2: Data Understanding – A data mining study is specific to addressing a well-defined business task, and different business tasks require different sets of data. Following the business understanding, the main activity of the data mining process is to identify the relevant data from many available databases.

Step 3: Data Preparation – The purpose of data preparation (or more commonly called data preprocessing) is to take the data identified in the previous step and prepare it for analysis by data mining methods. Compared to the other steps in CRISP-DM, data preprocessing consumes the most time and effort; most believe that this step accounts for roughly 80 percent of the total time spent on a data mining project Step 4: Model Building – Here, various modeling techniques are selected and applied to an already prepared data set in order to address the specific business need. The model-building step also encompasses the assessment and comparative analysis of the various models built.

Step 5: Testing and Evaluation – In step 5, the developed models are assessed and evaluated for their accuracy and generality. This step assesses the degree to which the selected model (or models) meets the business objectives and, if so, to what extent (i.e., do more models need to be developed and assessed).

Step 6: Deployment – Depending on the requirements, the deployment phase can be as simple as generating a report or as complex as implementing a repeatable data mining process across the enterprise. In many cases, it is the customer, not the data analyst, who carries out the deployment steps.

The most time demanding steps are 1-3.

Diff: 2 Page Ref: 264-270

13 .

74) Describe the role of the simple split in estimating the accuracy of classification models.

What is the most significant shortcoming of this data splitting process?

Answer: The simple split (or holdout or test sample estimation) partitions the data into two mutually exclusive subsets called a training set and a test set (or holdout set). It is common to designate two-thirds of the data as the training set and the remaining one-third as the test set. The training set is used by the inducer (model builder), and the built classifier is then tested on the test set. An exception to this rule occurs when the classifier is an artificial neural network. In this case, the data is partitioned into three mutually exclusive subsets: training, validation, and testing. Although it is a simple process, for small datasets it may not produce unbiased/homogeneous subsets, which can lead to inaccurate accuracy assessment results.

Diff: 2 Page Ref: 273 75) List and briefly describe five techniques (or algorithms) that are used for classification modeling.

Answer: • Decision tree analysis. Decision tree analysis (a machine-learning technique) is arguably the most popular classification technique in the data mining arena.

• Statistical analysis. Statistical techniques were the primary classification algorithm for many years until the emergence of machine-learning techniques. Statistical classification techniques include logistic regression and discriminant analysis.

• Neural networks. These are among the most popular machine-learning techniques that can be used for classification-type problems.

• Case-based reasoning. This approach uses historical cases to recognize commonalities in order to assign a new case into the most probable category.

• Bayesian classifiers. This approach uses probability theory to build classification models based on the past occurrences that are capable of placing a new instance into a most probable class (or category).

• Genetic algorithms. This approach uses the analogy of natural evolution to build directedsearch-based mechanisms to classify data samples.

• Rough sets. This method takes into account the partial membership of class labels to predefined categories in building models (collection of rules) for classification problems.

Diff: 2 Page Ref: 276-277

14 .

76) Describe cluster analysis and some of its applications. How does clustering differ from classification?

Answer: Cluster analysis is an exploratory data analysis tool for solving classification problems. The objective is to sort cases (e.g., people, things, events) into groups, or clusters, so that the degree of association is strong among members of the same cluster and weak among members of different clusters. Cluster analysis is an essential data mining method for classifying items, events, or concepts into common groupings called clusters. The method is commonly used in biology, medicine, genetics, social network analysis, anthropology, archaeology, astronomy, character recognition, and even in MIS development. As data mining has increased in popularity, the underlying techniques have been applied to business, especially to marketing. Cluster analysis has been used extensively for fraud detection (both credit card and e-commerce fraud) and market segmentation of customers in contemporary CRM systems.

Clustering is a unsupervised learning method, where there is no target variable, whereas classification is a supervised learning that uses (previously labeled) target variable.

Diff: 2 Page Ref: 280-283 77) Describe association rule mining. Provide the three metrics most commonly used to assess the quality and usefulness of association rules.

Answer: Association rule mining aims to find interesting relationships (affinities) between variables (items) in large databases. Because of its successful application to retail business problems, it is commonly called market-basket analysis. The main idea in market basket analysis is to identify strong relationships among different products (or services) that are usually purchased together (show up in the same basket together, either a physical basket at a grocery store or a virtual basket at an e-commerce Web site).

Metrics: • Support is the measure of how often the products and/or services appear together in the same transaction. Not that this is a bi-directional relationship.

• Confidence is the measure of how often certain products/services appear in a transaction given that some certain products/services are already in the transaction. Note that this is a unidirectional relationship.

• Lift is the ratio of the confidence of the rule and the expected confidence of the rule.

Diff: 3 Page Ref: 282-284 78) In lessons learned from the Target case, what legal warnings would you give another retailer using data mining for marketing?

Answer: If you look at this practice from a legal perspective, you would conclude that Target did not use any information that violates customer privacy; rather, they used transactional data that most every other retail chain is collecting and storing (and perhaps analyzing) about their customers. What was disturbing in this scenario was perhaps the targeted concept: pregnancy.

There are certain events or concepts that should be off limits or treated extremely cautiously, such as terminal disease, divorce, and bankruptcy.

Diff: 1 Page Ref: 293

15 .

79) Given the following notation "A: true positive count, B: true negative count, C: false positive count, and D: false negative count" write down the equations for overall accuracy, sensitivity, and specificity in terms of A, B, C, and D. Answer: overall accuracy = (A + B) / (A + B + C + D) sensitivity = A / (A + D) specificity = B / (B + C) Diff: 2 Page Ref: 274 80) List and briefly describe six common data mining mistakes.

Answer: • Selecting the wrong problem for data mining • Ignoring what your sponsor thinks data mining is and what it really can and cannot do • Leaving insufficient time for data preparation • Looking only at aggregated results and not at individual records • Being sloppy about keeping track of the data mining procedure and results • Ignoring suspicious findings and quickly moving on • Running mining algorithms repeatedly and blindly • Believing everything you are told about the data • Believing everything you are told about your own data mining analysis • Measuring your results differently from the way your sponsor measures them Diff: 2 Page Ref: 294-295

16 .

Business Intelligence, Analytics, Data Science, and AI, 5e (Sharda) Chapter 6 Predictive Analytics II: Text, Web, and Social Media Analytics 1) Text analytics is the subset of text mining that handles information retrieval and extraction, plus data mining.

Answer: FALSE Diff: 1 Page Ref: 305 2) Text mining is essentially the same as data mining, except for the addition of information retrieval and extensive preprocessing.

Answer: TRUE Diff: 2 Page Ref: 306 3) Articles and auxiliary verbs are assigned little value in text mining and are usually filtered out.

Answer: TRUE Diff: 1 Page Ref: 253 4) The benefits of text mining are obvious in the areas where very large amounts of structured and tabular data are being generated.

Answer: FALSE Diff: 2 Page Ref: 306 5) Foreign and regional accents present challenges for natural language processing.

Answer: TRUE Diff: 2 Page Ref: 309 6) In NLP, part-of-speech tagging refers to the word sense disambiguation (i.e., words having more than one meaning).

Answer: FALSE Diff: 2 Page Ref: 309 7) It is a long-standing dream of the artificial intelligence community to have algorithms that are capable of automatically reading and obtaining knowledge from text.

Answer: TRUE Diff: 1 Page Ref: 309 8) WordNet is a laboriously hand-coded database of sentiment scores of English words.

Answer: FALSE Diff: 2 Page Ref: 310 9) Similar to CRISP-DM standard process, text mining process is also composed of six consecutive tasks.

Answer: FALSE Diff: 2 Page Ref: 319

1 .

10) The outcome of the text mining tasks "introduce structure to the corpus" is a term-bydocument matrix.

Answer: TRUE Diff: 1 Page Ref: 319 320 11) In text mining, the data mining task named classification is often called text clustering.

Answer: FALSE Diff: 2 Page Ref: 321 12) Confidence in association analysis (in text mining) refers to the percentage (or number) of documents that include all of the concepts.

Answer: FALSE Diff: 3 Page Ref: 322 13) In sentiment analysis, sentiment suggests a transient, temporary opinion reflective of one's feelings.

Answer: FALSE Diff: 2 Page Ref: 326 14) Current use of sentiment analysis in voice of the customer applications allows companies to change their products or services in real time in response to perceived customer opinions.

Answer: TRUE Diff: 2 Page Ref: 330 15) In sentiment analysis, it is hard to classify some subjects such as news as good or bad, but easier to classify others, e.g., movie reviews, in the same way.

Answer: TRUE Diff: 2 Page Ref: 333 16) In sentiment analysis, we can only assign sentiment to subjective words/terms; the objective terms do not have sentiment score.

Answer: TRUE Diff: 2 Page Ref: 332 17) A lexicon is essentially a collection of textual documents to be critically analyzed with text mining process.

Answer: FALSE Diff: 2 Page Ref: 334 18) Web mining is the process of discovering intrinsic relationships (i.e., interesting, and useful information) from Web data, which are expressed in the form of textual, linkage, or usage information.

Answer: TRUE Diff: 2 Page Ref: 291

2 .

19) Search engine optimization techniques do not play a major role in a Web site's search ranking because only well-written and structured Web content matters.

Answer: FALSE Diff: 2 Page Ref: 347 20) Clickstream analysis does not need users to enter their perceptions of the Web site or other feedback directly, to be useful in determining their preferences.

Answer: TRUE Diff: 2 Page Ref: 349-350 21) A typical internet search engine is composed of only two main processes, development cycle and response cycle.

Answer: TRUE Diff: 2 Page Ref: 344-345 22) Search engine optimization is the intentional activity of improving the speed/agility and accuracy of a given search engine.

Answer: FALSE Diff: 3 Page Ref: 347 23) Topic modeling is a suite of probabilistic machine learning algorithms that aim to discover and annotate large archives of documents with thematic information.

Answer: TRUE Diff: 2 Page Ref: 336 24) In the evolution of social media user engagement, the largest recent change is the growth of creators.

Answer: FALSE Diff: 2 Page Ref: 358 25) Social network analysis uses simple statistics to identify activity characteristics and trends, such as how many followers you have, how many reviews were generated on Facebook, and which channels are being used most often.

Answer: FALSE Diff: 2 Page Ref: 361 26) In the opening vignette, the architectural system that supported Watson used all the following elements, except?

A) Massive parallelism to enable simultaneous consideration of multiple hypotheses B) An underlying confidence subsystem that ranks and integrates answers C) A core engine that could operate seamlessly in another domain without changes D) Integration of shallow and deep knowledge Answer: C Diff: 2 Page Ref: 303

3 .

27) In text mining, tokenizing is the process of ________.

A) Breaking a block of text into its lowest level components B) Reducing multiple words to their base or root C) Transforming the term-by-document matrix to a manageable size D) Creating new branches or stems of recorded paragraphs Answer: A Diff: 2 Page Ref: 307 28) All of the following are challenges associated with natural language processing, except?

A) Dividing up a text into individual words in English B) Understanding the context in which something is said C) Distinguishing between words that have more than one meaning D) Recognizing typographical or grammatical errors in texts Answer: A Diff: 2 Page Ref: 309 29) Natural language processing (NLP) is associated with which of the following areas?

A) Text mining B) Artificial intelligence C) Computational linguistics D) All of the other answers are true Answer: D Diff: 1 Page Ref: 308 30) Which of the following is not a concept related to information retrieval?

A) Search engines B) Link analysis C) Topic modeling D) Document matching Answer: C Diff: 3 Page Ref: 305 31) In sentiment analysis, which of the following is an implicit opinion?

A) The hotel we stayed in was terrible B) The customer service I got for my TV was laughable C) The cruise we went on last summer was a disaster D) Our new mayor is great for the city Answer: B Diff: 3 Page Ref: 327 32) Which of the following is the best definition for corpus?

A) It is an unsupervised machine learning algorithm B) It is a sentiment C) It is a collection of textual documents D) It is a supervised machine learning algorithm Answer: C Diff: 2 Page Ref: 307 4 .

33) What do voice of the market applications of sentiment analysis do?

A) They examine customer sentiment at the aggregate level B) They examine employee sentiment in the organization C) They examine the stock market for trends D) They examine the "market of ideas" in politics Answer: A Diff: 2 Page Ref: 331 34) Sentiment analysis projects require a lexicon for use. If a project in English is undertaken, you must generally make sure to ________.

A) Use only the single, approved English lexicon B) Use any general English lexicon C) Use an English lexicon appropriate to the project at your discretion D) Create an English lexicon for the project Answer: C Diff: 1 Page Ref: 332 35) In text analysis, what is a lexicon?

A) A catalog of words, their synonyms, and their meanings B) A catalog of customer reviews, their words, and phrases C) A catalog of explicit letters, words, phrases, and sentences D) A catalog of customers, products, and services Answer: A Diff: 2 Page Ref: 332 36) What types of documents are best suited for semantic labeling and aggregation to determine sentiment orientation?

A) Medium- to large-sized documents B) Small- to medium-sized documents C) Large-sized documents D) Any size of documents Answer: B Diff: 3 Page Ref: 336 37) What does Web content mining involve?

A) Analyzing the universal resource locators in Web pages B) Analyzing the unstructured text on Web pages C) Analyzing the pattern of visits to Web pages D) Analyzing the XML and other metadata of Web pages Answer: B Diff: 2 Page Ref: 342

5 .

38) Breaking up a Web page into its components to identify worthy words/terms and indexing them using a set of rules is called ________.

A) Preprocessing the documents B) Document analysis C) Creating the term-by-document matrix D) Parsing the documents Answer: D Diff: 3 Page Ref: 345 39) Search engine optimization is a process by which ________.

A) Web site developers can negotiate better deals for paid ads B) Web site developers can increase their site's search rankings C) Search engine developers can increase search engine's efficiency D) Web site developers optimize the aesthetic features of their Web sites Answer: B Diff: 2 Page Ref: 347 40) What are the two main types of Web analytics?

A) Old-school and new-school Web analytics B) Bing and Google based Web analytics C) Off-site and on-site Web analytics D) Data-based and subjective Web analytics Answer: C Diff: 3 Page Ref: 350 41) Web site usability metrics include all of the following metrics, except?

A) Number of downloads B) Time spent on the site C) User profiles D) Page views Answer: C Diff: 2 Page Ref: 351 42) Understanding which keywords your users enter to reach your Web site through a search engine can help you understand ________.

A) The hardware your Web site is running on B) The type of Web browser being used by your Web site visitors C) Most of your Web site visitors' wants and needs D) How well visitors understand your products Answer: D Diff: 3 Page Ref: 353

6 .

43) Which of the following statements is not true about Web site conversion statistics?

A) Web site visitors can be classed as either new or returning B) Visitors who begin a purchase on Web sites must complete it C) The conversion rate is the number of people who act divided by the number of visitors D) Analyzing exit rates can tell you why visitors left your Web site Answer: B Diff: 3 Page Ref: 353-354 44) What is one major way in which Web-based social media differs from traditional publishing media?

A) Most Web-based media are operated by the government and large firms B) They use different languages structures for publication C) They have different costs to own and operate D) Web-based media have a narrower range of quality Answer: C Diff: 3 Page Ref: 358 45) What does advanced analytics for social media do?

A) It helps identify your followers B) It identifies links between groups C) It examines the content of online conversations D) It identifies the biggest sources of influence online Answer: C Diff: 2 Page Ref: 361 46) IBM's Watson utilizes a massively parallel, text mining—focused, probabilistic evidencebased computational architecture called ________.

A) Supercomputer B) Deep blue C) Jeopardy!

D) DeepQA Answer: D Diff: 1 Page Ref: 303 47) ________, also called homonyms, are syntactically identical words with different meanings.

A) Antonyms B) Polysemes C) Morphology D) Tokens Answer: B Diff: 2 Page Ref: 307

7 .

48) When a word has more than one meaning, selecting the meaning that makes the most sense can only be accomplished by taking into account the context within which the word is used. This concept is commonly known as ________.

A) Word sense disambiguation B) Stemming analytics C) Morphology determination D) Token identification Answer: A Diff: 3 Page Ref: 309 49) The technique that is used to detect favorable and unfavorable opinions toward specific products and services using large numbers of textual data sources.

A) Computational linguistic B) Sentiment analysis C) Morphology determination D) Part-of-speech tagging Answer: B Diff: 2 Page Ref: 310 50) ________ is a laboriously hand-coded database of English words, their definitions, sets of synonyms, and various semantic relations between synonym sets.

A) Computational linguistic B) WordNet C) Morphology D) Corpus Answer: B Diff: 1 Page Ref: 310 51) Which of the following is not one of the high-level tasks in text mining process?

A) Create the Term-Document Matrix B) Extract Knowledge C) Identify the Problem D) Establish the Corpus Answer: C Diff: 2 Page Ref: 318-319 52) Which of the following is not among the methods used to reduce the size of the rather sparse term-document matrix?

A) Use singular-value decomposition B) Use domain experts' knowledge C) Eliminate the terms with very few occurrences in very few documents D) Use statistical correlation analysis Answer: D Diff: 3 Page Ref: 321

8 .

53) ________ is mostly driven by sentiment analysis and is a key element of customer experience management initiatives, where the goal is to create an intimate relationship with the customer.

A) Consumer market analytics B) Brand management C) Voice of the customer D) Competitive intelligence Answer: C Diff: 1 Page Ref: 330 54) When viewed as a binary feature, ________ is the binary classification task of labeling an opinionated document as expressing either an overall positive or an overall negative opinion.

A) Sentiment detection B) Text mining C) Polarity classification D) Target identification Answer: C Diff: 2 Page Ref: 332 55) Which of the following is not among the web mining categories?

A) Content mining B) Source mining C) Usage mining D) Structure mining Answer: B Diff: 2 Page Ref: 341 56) In the search engine development process, which of the following enablers are usually used to automatically read through the contents of Web sites?

A) Spiders B) Regressors C) Classifiers D) Translators Answer: A Diff: 1 Page Ref: 342 57) A(n) ________ is one or more Web pages that provide a collection of links to authoritative Web pages.

A) Entry page B) Home page C) Hub D) Spoke Answer: C Diff: 1 Page Ref: 343

9 .

58) Which of the following allows extracting useful information from the links embedded in Web documents?

A) Text mining B) Social media mining C) Web content mining D) Web structure mining Answer: D Diff: 2 Page Ref: 343 59) Which of the following is not one of the components in the development cycle of search engines?

A) Crawler B) Ranker C) Indexer D) Scheduler Answer: B Diff: 2 Page Ref: 345 60) In search engines, which of the following is not a task performed by document indexer?

A) Preprocessing the documents B) Parsing the documents C) Analyzing the documents D) Creating the term-by-document matrix Answer: C Diff: 3 Page Ref: 345-346 61) Which of the following is not a source for web traffic that was generated by your various offline or online advertising campaigns?

A) Search engines B) Referral websites C) Direct entries D) Word of mouth Answer: D Diff: 2 Page Ref: 352 62) Which of the following helps you understand whether your specific marketing objective for a Web page is being achieved?

A) Descriptive statistics B) Parsing accuracy C) Conversion statistics D) Inferential statistics Answer: C Diff: 3 Page Ref: 345-353

10 .

63) Which of the following is not among the best practices for social media analytics?

A) Think of measurement as a guidance system, not a rating system B) Track the elusive users C) Identify your most powerful influencers D) Look beyond the brand Answer: B Diff: 3 Page Ref: 361 64) ________ is a connections metric for social networks that measures the ties that actors in a network have with others that are geographically close.

A) Propinquity B) Mutuality C) Multiplexity D) Homophily Answer: A Diff: 3 Page Ref: 356 65) ________ is a segmentation metric for social networks that measures the strength of the bonds between actors in a social network.

A) Propinquity B) Cohesion C) Multiplexity D) Cliques and social circles Answer: B Diff: 2 Page Ref: 357 66) Which of the following is not among the topic modeling approaches?

A) Singular value decomposition B) Latent semantic analysis C) Latent Dirichlet allocation D) Latent semantic indexing Answer: A Diff: 2 Page Ref: 337-338 67) Which of the following is a popular topic modeling approach?

A) Singular value decomposition B) Inferential statistical analysis C) Latent Dirichlet allocation D) Causality detection Answer: C Diff: 2 Page Ref: 337-338

11 .

68) In Latent Dirichlet allocation, which of the following is considered latent?

A) Documents B) Topics C) Words D) Indexes Answer: B Diff: 3 Page Ref: 338-339 69) Which of the following is not among the tasks that NLP has successfully been applied to?

A) Question answering B) Summarization C) Semantic understanding D) Speech recognition Answer: C Diff: 2 Page Ref: 312 70) In sentiment analysis, which of the following is the dominant techniques used for identification of polarity at the word/term level?

A) Using a lexicon B) Using morphology C) Using singular value decomposition D) Using principal component analysis Answer: A Diff: 2 Page Ref: 334 71) Define text mining. How does text mining relate to text analytics, data mining, and NLP? Answer: Text mining (also known as text data mining or knowledge discovery in textual databases) is the semi-automated process of extracting patterns (useful information and knowledge) from large amounts of unstructured data sources.

Text mining is part test analytics which is a larger concept. Text mining uses NLP (for text characterization and structuring of textual data) and Data Mining (for pattern extraction from the structured textual data) as its enablers.

Diff: 1 Page Ref: 305-306

12 .

72) Natural language processing (NLP), a subfield of artificial intelligence and computational linguistics, is an important component of text mining. What is the definition of NLP? List and briefly describe three key challenges in NLP. Answer: NLP is a discipline that studies the problem of "understanding" the natural human language, with the view of converting depictions of human language into more formal representations in the form of numeric and symbolic data that are easier for computer programs to manipulate.

Challenges — • Part-of-speech tagging • Text segmentation • Word sense disambiguation • Syntactic ambiguity • Imperfect or irregular input • Speech acts Diff: 2 Page Ref: 308-309 73) In the security domain, one of the largest and most prominent text mining applications is the highly classified ECHELON surveillance system. What is ECHELON assumed to be capable of doing? In your opinion, what are the most important advantages and disadvantages of these systems?

Answer: Identifying the content of telephone calls, faxes, e-mails, and other types of data and intercepting information sent via satellites, public switched telephone networks, and microwave links.

Advantages — identifying and precenting terrorist attacks Disadvantages — privacy invasion Diff: 2 Page Ref: 313 74) Describe the text mining process. Make sure to comment on the activities performed and results obtained in each step.

Answer: At a very high level, the text mining process can be broken down into three consecutive tasks, each of which has specific inputs to generate certain outputs.

1. Establish the Corpus: Collect and organize domain-specific unstructured data. Output: Collection of documents 2. Create the Term-Document Matrix: Introduce structure to the corpus. Output: T-D matrix insight 3. Extract Knowledge: Discover novel patterns from the T-D matrix. Output: actionable insight Diff: 2 Page Ref: 318-320

13 .

75) List and briefly describe each of the steps in the sentiment analysis process.

Answer: 1. Sentiment Detection: Here the goal is to differentiate between a fact and an opinion, which may be viewed as classification of text as objective or subjective.

2. N-P Polarity Classification: Given an opinionated piece of text, the goal is to classify the opinion as falling under one of two opposing sentiment polarities, or locate its position on the continuum between these two polarities.

3. Target Identification: The goal of this step is to accurately identify the target of the expressed sentiment.

4. Collection and Aggregation: In this step all text data points in the document are aggregated and converted to a single sentiment measure for the whole document.

Diff: 2 Page Ref: 332-334 76) In what ways does the Web pose great challenges for effective and efficient knowledge discovery through text mining?

Answer: • The Web is too big for effective data mining. The Web is so large and growing so rapidly that it is difficult to even quantify its size. Because of the sheer size of the Web, it is not feasible to set up a data warehouse to replicate, store, and integrate all of the data on the Web, making data collection and integration a challenge.

• The Web is too complex. The complexity of a Web page is far greater than a page in a traditional text document collection. Web pages lack a unified structure. They contain far more authoring style and content variation than any set of books, articles, or other traditional textbased document.

• The Web is too dynamic. The Web is a highly dynamic information source. Not only does the Web grow rapidly, but its content is constantly being updated. Blogs, news stories, stock market results, weather reports, sports scores, prices, company advertisements, and numerous other types of information are updated regularly on the Web. • The Web is not specific to a domain. The Web serves a broad diversity of communities and connects billions of workstations. Web users have very different backgrounds, interests, and usage purposes. Most users may not have good knowledge of the structure of the information network and may not be aware of the heavy cost of a particular search that they perform.

• The Web has everything. Only a small portion of the information on the Web is truly relevant or useful to someone (or some task). Finding the portion of the Web that is truly relevant to a person and the task being performed is a prominent issue in Web-related research.

Diff: 2 Page Ref: 340 77) What is search engine optimization (SEO) and why is it important for organizations that own Web sites?

Answer: Search engine optimization (SEO) is the intentional activity of affecting the visibility of an e-commerce site or a Web site in a search engine's natural (unpaid or organic) search results. In general, the higher ranked on the search results page, and more frequently a site appears in the search results list, the more visitors it will receive from the search engine's users.

Being indexed by search engines like Google, Bing, and Yahoo! is not good enough for businesses. Getting ranked on the most widely used search engines and getting ranked higher than your competitors are what make the difference.

Diff: 2 Page Ref: 347 14 .

78) What is the difference between white hat and black hat SEO activities?

Answer: An SEO technique is considered white hat if it conforms to the search engines' guidelines and involves no deception. Because search engine guidelines are not written as a series of rules or commandments, this is an important distinction to note. White-hat SEO is not just about following guidelines, but about ensuring that the content a search engine indexes and subsequently ranks is the same content a user will see.

Black-hat SEO attempts to improve rankings in ways that are disapproved by the search engines, or involve deception or trying to trick search engine algorithms from their intended purpose.

Diff: 2 Page Ref: 348-349 79) Why are the users' page views and time spent on your Web site important metrics?

Answer: If people come to your Web site and don't view many pages, that is undesirable and your Web site may have issues with its design or structure. Another explanation for low page views is a disconnect in the marketing messages that brought them to the site and the content that is actually available.

Generally, the longer a person spends on your Web site, the better it is. That could mean they're carefully reviewing your content, utilizing interactive components you have available, and building toward an informed decision to buy, respond, or take the next step you've provided. On the contrary, the time on site also needs to be examined against the number of pages viewed to make sure the visitor isn't spending his or her time trying to locate content that should be more readily accessible.

Diff: 3 Page Ref: 350 80) What are the three categories of social media analytics technologies and what do they do?

Answer: • Descriptive analytics: Uses simple statistics to identify activity characteristics and trends, such as how many followers you have, how many reviews were generated on Facebook, and which channels are being used most often.

• Social network analysis: Follows the links between friends, fans, and followers to identify connections of influence as well as the biggest sources of influence.

• Advanced analytics: Includes predictive analytics and text analytics that examine the content in online conversations to identify themes, sentiments, and connections that would not be revealed by casual surveillance.

Diff: 2 Page Ref: 361

15 .

Business Intelligence, Analytics, Data Science, and AI, 5e (Sharda) Chapter 7 Deep Learning and Cognitive Computing 1) In the opening vignette, banks had begun to use deep learning to provide more accurate prediction of the customer churn.

Answer: FALSE Diff: 2 Page Ref: 369 2) Deep learning is a separate branch of computational sciences from AI. Answer: FALSE Diff: 1 Page Ref: 373 3) Structured, human-mediated machine-learning approaches have been working fine for rather abstract and formal tasks.

Answer: TRUE Diff: 2 Page Ref: 373 4) The initial idea for deep learning began fairly recently, right around 2013.

Answer: FALSE Diff: 2 Page Ref: 374 5) Neurons are processing units that perform a set of predefined mathematical operations on the numerical values coming from the input variables or from the other neuron outputs to create and push out its own outputs.

Answer: TRUE Diff: 2 Page Ref: 376 6) In artificial neurons weight terms are adjustable but biased terms are fixed.

Answer: FALSE Diff: 2 Page Ref: 376 7) The task undertaken by a neural network does not affect the architecture of the neural network; in other words, architectures are problem independent.

Answer: FALSE Diff: 2 Page Ref: 381-382 8) In neural network training, the output of the transfer function is fed into the summation function.

Answer: FALSE Diff: 3 Page Ref: 382 9) Neural network training is usually done by defining a performance function.

Answer: TRUE Diff: 1 Page Ref: 385

1 .

10) Deep neural networks are referred to as "deep" because they are primarily used to evaluate substantially deeper philosophical issues.

Answer: FALSE Diff: 1 Page Ref: 395 11) The growth in the use of deep neural networks has been spurred by advancements in GPU hardware technology.

Answer: TRUE Diff: 2 Page Ref: 395 12) Feedforward neural network models allow both forward and backward connections between neurons.

Answer: FALSE Diff: 2 Page Ref: 396 13) Neural networks are called "black boxes" due to the lack of ability to explain their reasoning.

Answer: TRUE Diff: 2 Page Ref: 390 14) CNNs are deep learning architectures that are designed to process only the image data sets.

Answer: FALSE Diff: 2 Page Ref: 400 15) ANN are capable of solving complex problems and provide detailed explanations of their capabilities.

Answer: FALSE Diff: 2 Page Ref: 389-390 16) In CNN type deep learning models, the convolution layers are often followed by a pooling layer.

Answer: TRUE Diff: 3 Page Ref: 401 17) Deep MLP and convolutional networks are specialized for processing a sequential grid of values.

Answer: FALSE Diff: 3 Page Ref: 412 18) RNN basically models a dynamic system with sequential inputs.

Answer: TRUE Diff: 2 Page Ref: 412 19) Compute unified device architecture, which enables software developers to use GPUs, was originally designed by Google.

Answer: FALSE Diff: 2 Page Ref: 420 2 .

20) In Caffe, everything is done using text files instead of code.

Answer: TRUE Diff: 2 Page Ref: 421 21) Theano is a Java library developed by the Deep Learning Group at the University of Montreal.

Answer: FALSE Diff: 3 Page Ref: 422 22) Torch is an open-source neural network library written in Python that functions as a highlevel application programming interface (API).

Answer: FALSE Diff: 3 Page Ref: 421-422 23) Cognitive computing addresses highly complex situations that are characterized by ambiguity and uncertainty; in other words, it handles the kinds of problems that are thought to be solvable by human ingenuity and creativity.

Answer: TRUE Diff: 2 Page Ref: 425 24) The main goal of cognitive computing is not to augment human capability, rather it is to automate complex processes by acting like a human in decision situations.

Answer: FALSE Diff: 2 Page Ref: 427 25) Representation learning is a subcategory of deep learning that focuses on better representation of complex problems that require unsupervised learning.

Answer: FALSE Diff: 3 Page Ref: 374 26) In the opening vignette, which of the following was not part of the obtained results?

A) Elimination of fraud completely at 15% of branches B) Reduction in false positives by 60% C) Increase in true positives by 50% D) Resources focused on actual cases of fraud Answer: A Diff: 2 Page Ref: 370 27) Which of the following is not among the factors allowing banks to use more advanced AI based fraud detection systems?

A) Evolving new learning systems enabled by AI B) Hardware and processors becoming more powerful C) Specialized algorithms becoming available D) Security and privacy of data is becoming less of an issue Answer: D Diff: 2 Page Ref: 370 3 .

28) Which type of machine learning system is represented by the following process: input--to-manually created representation--to--output?

A) Knowledge-based systems B) Classic machine learning C) Generic representation learning D) Deep learning Answer: A Diff: 2 Page Ref: 375 29) Which type of learning system is represented by the following process: input--simple features--more advanced features--mapping from features--output?

A) Knowledge-based systems B) Classic machine learning C) Generic representation learning D) Deep learning Answer: D Diff: 2 Page Ref: 375 30) Which type of transfer function is described as "operation increases in a straight line based on the operation of a=n"?

A) Hard limit B) Linear C) Log sigmoid D) Rectified linear Answer: B Diff: 2 Page Ref: 377 31) ________ perform a set of predefined mathematical operations on the numerical values coming from the input variables or from the other neuron outputs to create and push out its own outputs.

A) ANN architecture B) Momentum C) Cognitive limit function D) Processing element Answer: D Diff: 2 Page Ref: 376 32) In developing an artificial neural network, all of the following are important reasons to preselect the network architecture and learning method, except?

A) Some configurations have better success than others with specific problems B) Development personnel may be more experienced with certain architectures C) Most neural networks need special purpose hardware, which may be absent D) Some neural network software may not be available in the organization Answer: C Diff: 2 Page Ref: 385

4 .

33) Backpropagation learning algorithms for neural networks ________.

A) Are the least popular algorithm due to their inaccuracy B) Are used without hidden layers for effectiveness C) Are used without a training set of data for efficiency D) Require having an error tolerance set in advance Answer: D Diff: 2 Page Ref: 387 34) Sensitivity analysis is a method for extracting what among the inputs and the outputs of a trained neural network model?

A) Underlying constraints B) Cause-and-effect relationships C) Bias D) Detailed quantitative predictive models Answer: B Diff: 2 Page Ref: 390 35) GPU hardware is often used for deep neural networks. What does GPU stand for?

A) Game programming unit B) Graphics processing unit C) Gated power unit D) Gated processing unit Answer: B Diff: 2 Page Ref: 395 36) Which of the following does not describe a deep feedforward network?

A) Most general type of deep network B) Many layers of neurons C) Network connections can be unidirectional or bidirectional D) Tensors are handled as input Answer: C Diff: 3 Page Ref: 343 37) Which type of neural network is characterized by allowing feedback connections?

A) Deep feedforward network B) Feedforward network C) Recurrent neural network D) None of these Answer: C Diff: 2 Page Ref: 343

5 .

38) Why is pretraining a deep MLP network appropriate?

A) It increases the chances of a global optimum being reached B) It is always necessary for this type of network C) It increases the duration before global optimum is reached D) It helps in restructuring the input variable space Answer: A Diff: 2 Page Ref: 396 39) What concept do CNN architectures use to avoid large numbers of weight parameters being defined and trained?

A) Convolutional theory B) Parameter sharing C) Filtering D) Gradual boosting Answer: B Diff: 2 Page Ref: 401-402 40) Real applications of deep learning in general and CNN in particular highly depend on the availability of ________.

A) Large, unsorted data sets B) Moderately sized, unsorted data sets C) Large, annotated data sets D) Small, annotated, randomized data sets Answer: C Diff: 2 Page Ref: 404 41) In CNN type deep learning models, most of the time, a convolution layer is followed by which of the following type of component?

A) Summation function B) Transfer function C) Polling layer D) Output layer Answer: D Diff: 2 Page Ref: 403 42) LSTM networks are typically not used for what type of application and/or data types?

A) Handwriting recognition B) Speech recognition C) Image recognition D) Machine translation Answer: C Diff: 2 Page Ref: 415

6 .

43) Which deep learning framework supports TPUs (a type of processor developed by Google)?

A) Torch B) Caffe C) TensorFlow D) Theano Answer: C Diff: 2 Page Ref: 422 44) Which deep learning framework is based on LuaJIT library, which is a compiled version of the popular Lua programming language?

A) Torch B) Caffe C) TensorFlow D) Theano Answer: A Diff: 2 Page Ref: 421 45) Which of the following deep learning frameworks is not a library but an application programming interface that can run on top of various deep learning frameworks?

A) Torch B) Keras C) TensorFlow D) Theano Answer: B Diff: 2 Page Ref: 422 46) Which of the following is ab AI-based learning technique that emphasizes learning and discovering features by the system in addition to discovering the mapping from those features to the output/target?

A) Machine learning B) Deep learning C) Reinforcement learning D) Representation learning Answer: D Diff: 2 Page Ref: 373 47) Machine learning is a subset of which of the following?

A) Artificial intelligence B) Knowledge based systems C) Generic programming D) Representation learning Answer: A Diff: 1 Page Ref: 374

7 .

48) Which of the following has greatly compensated for the lack of intimate knowledge and reduced the level of skill needed for implementing deep neural networks?

A) Graphic processing units B) Fast CPU architectures C) Large training data sets D) Representation learning Answer: C Diff: 2 Page Ref: 375 49) As it is being practiced today, ________ seems to be nothing but an extension of neural networks capable of handling more complicated tasks with a higher level of sophistication.

A) Deep learning B) Rule based expert systems C) Fuzzy inference systems D) Support vector machines Answer: A Diff: 2 Page Ref: 374 50) Which of the following is the difference between knowledge-based systems and classical machine learning?

A) Simple features B) Auto created features C) Mapping from features D) Manually created representation Answer: C Diff: 3 Page Ref: 375 51) In neural networks, which of the following is tasked to compute the weighted sums of all input elements entering each processing element?

A) Transfer function B) Backpropagation C) Learning rate D) Summation function Answer: D Diff: 1 Page Ref: 382 52) ________ are among the key learning elements of an ANNs that represent the relative strength (or mathematical value) of connections between layers.

A) Transfer function B) Backpropagation C) Connection weights D) Summation function Answer: C Diff: 2 Page Ref: 382

8 .

53) ________ is the most widely used supervised learning algorithm in neural computing.

A) Transfer function B) Backpropagation C) Connection weights D) Summation function Answer: B Diff: 2 Page Ref: 386 54) Which of the following is usually used to pretrain the network parameters that employs some unsupervised deep learning methods?

A) Deep knowledge representation B) Backpropagation C) Deep belief networks D) Self organizing maps Answer: C Diff: 2 Page Ref: 396 55) Generally, a sequential order of layers must be held between the input and the output layers in this type of network architecture.

A) RNN B) SVM C) Knowledge based systems D) MLP Answer: D Diff: 2 Page Ref: 396 56) ________ is among the most popular techniques proposed for shedding light into the "blackbox" characterization of trained neural networks.

A) Regression analysis B) Sensitivity analysis C) Transparency analysis D) Explanatory statistics Answer: B Diff: 2 Page Ref: 390 57) Which of the following is not among the sensitivity analysis techniques used to explain neural network models?

A) Observing the hyperparameters in a SVM model B) Investigating the structure of a developed decision tree C) Observing the changes in output variable while perturbing the input variable values D) Developing a simple surrogate model for explanation purposes Answer: A Diff: 3 Page Ref: 390

9 .

58) Which of the following is not true about Word2vec?

A) It is a text mining/processing method B) It employs a two-layer neural network C) Word2vec is essentially a deep learning algorithm D) It is developed and published by Google Answer: C Diff: 2 Page Ref: 408 59) Which of the following applications uses deep neural network algorithms (along with other AI techniques) to deliver information about the images captured by users from their nearby objects?

A) Pinterest B) Snap Chat C) Image Net D) Google Lens Answer: D Diff: 2 Page Ref: 406 60) Which of the following is an ongoing research project that provides researchers with a large database of images, each linked to a set of synonym words from WordNet? A) PicWorld B) SentiWordNet C) ImageNet D) GoogLeNet Answer: C Diff: 2 Page Ref: 404 61) ________ is a variation of the deep MLP architecture, initially designed for computer vision applications (e.g., image processing, video processing, text recognition) but are also applicable to nonimage data sets.

A) RNN B) CNN C) LSTM D) SVM Answer: B Diff: 2 Page Ref: 400 62) ________, a very popular NLP application, is trained on an enormous corpus of text data from books, websites, articles, and a variety of other human language sources to answer virtually any question.

A) Google Translate B) CNN C) ChatGPT D) Google Lens Answer: C Diff: 1 Page Ref: 415 10 .

63) Which of the following languages is usually utilized to program GPUs? A) CUDA B) Python C) Java D) Perl Answer: A Diff: 2 Page Ref: 420 64) ________, often used interchangeably with AI, is the umbrella term used for technologies that rely on data and scientific methods/computations to make (or help/support in making) decisions.

A) Cognitive computing B) Quantum computing C) CUDA D) Computational linguistic Answer: A Diff: 2 Page Ref: 425 65) Which of the following is not among the input to the cognitive computing process?

A) Complex problems B) AI algorithms C) Both structures and unstructured data D) Quantum computing Answer: D Diff: 2 Page Ref: 426 66) All of the following are attributes of cognitive computing, except?

A) Interpretive B) Interactive C) Contextual D) Adaptive Answer: A Diff: 2 Page Ref: 426-427 67) Typical use cases for cognitive computing include all of the following, except?

A) Speech recognition B) Fraud detection C) Sentiment analysis D) Churn analysis Answer: D Diff: 2 Page Ref: 428

11 .

68) Cognitive search differs from traditional search in all of the following ways, except?

A) Employ advanced AI technologies B) Can contextualize the search space C) Can produce results faster D) Can handle a variety of data types Answer: C Diff: 2 Page Ref: 429 69) Which of the following is the most evolved search engine method?

A) Semantic search B) Contextual search C) Cognitive search D) Keyword search Answer: C Diff: 2 Page Ref: 430 70) What is MYCIN? A) A deep learning architecture B) An expert system to diagnose and treat bacterial diseases C) An AI system to detect and prevent network intrusions D) A smart search engine developed using cognitive computing Answer: B Diff: 2 Page Ref: 432 71) List and briefly describe the key attributes of cognitive computing systems.

Answer: Cognitive computing systems must have the following key attributes as defined by the Cognitive Computing Consortium (2018): • Adaptive: Cognitive systems must be flexible enough to learn as information changes and goals evolve. The systems must be able to digest dynamic data in real time and make adjustments as the data and environment change.

• Interactive: Human-computer interaction (HCI) is a critical component in cognitive systems.

Users must be able to interact with cognitive machines and define their needs as those needs change. The technologies must also be able to interact with other processors, devices, and cloud platforms.

• Iterative and stateful: Cognitive computing technologies can also identify problems by asking questions or pulling in additional data if a stated problem is vague or incomplete. The systems do this by maintaining information about similar situations that have previously occurred.

• Contextual: Understanding context is critical in thought processes, so cognitive systems must understand, identify, and mine contextual data, such as syntax, time, location, domain, requirements, and a specific user's profile, tasks, or goals. Cognitive systems may draw on multiple sources of information, including structured and unstructured data and visual, auditory, or sensor data.

Diff: 3 Page Ref: 426-427

12 .

72) How does cognitive computing differ from artificial intelligence?

Answer: • Technologies - both technologies utilize machine learning, and LP, neural networks and deep learning: but cognitive computing adds text mining and sentiment analysis • Capabilities offered - while AI seeks to find hidden patterns in data sources, cognitive computing attempts to simulate the human thought process to find solutions • Purpose - AI seeks to automate complex processes whereas cognitive computing seeks to augment human capability • Industries - both have application in most industries, but the focus of cognitive computing is currently in customer service, marketing, healthcare, entertainment and services Diff: 2 Page Ref: 427 73) What is DeepQA in IBM's Watson? List and briefly describe at least four major application areas for IBM Watson.

Answer: DeepQA is the core technology behind IBM Watson, which is an extraordinary computer system–a novel combination of advanced hardware and software–designed to answer questions posed in natural human language.

Major application areas of IBM Watson: • Healthcare and medicine • Security • Finance • Retail • Education • Government • Scientific research Diff: 2 Page Ref: 432-433 74) List, briefly define, and exemplify the four taxonomical layers often captured in a Venn diagram to describe the relationship between AI and deep learning.

Answer: Artificial Intelligence — • Knowledge based/expert systems • Fuzzy logic • Robotics Machine Learning • Decision trees • Logistic regression • SVM Representation Learning • Autoencoders • Clustering • PCA/ICA Deep Learning • CNN • RNN • LSTM Diff: 2 Page Ref: 373-374 13 .

75) List and describe the nine steps in the development process for an ANN application.

Answer: 1. Collect, organize, and format the data 2. Separate data into training, validation, and testing sets 3. Decide on a network architecture and structure 4. Select a learning algorithm 5. Set network parameters and initialize their values 6. Initialize weights and start training (and validation) 7. Stop training, freeze the network weights 8. Test the trained network 9. Deploy the network for use on unknown new cases Diff: 3 Page Ref: 383-385 76) What are the five steps in the backpropagation learning algorithm? List and describe.

Answer: 1. Initialize weights with random values and set other parameters.

2. Read in the input vector and the desired output.

3. Compute the actual output via the calculations, working forward through the layers.

4. Compute the error.

5. Change the weights by working backward from the output layer through the hidden layers.

Diff: 3 Page Ref: 387 77) Define the term sensitivity analysis as it relates to ANNs. List and briefly describe at least three different sensitivity analysis methods.

Answer: Sensitivity analysis is a method for extracting the cause-and-effect relationships among the inputs and the outputs of a trained neural network model.

The alternative sensitivity analysis methods include the following: • Developing and observing a well-trained decision tree model • Developing and observing a rich and large random forest model • Sensitivity analysis based on input value perturbation • Sensitivity analysis based on leave-one-out methodology • Sensitivity analysis based on developing a surrogate model Diff: 3 Page Ref: 390 78) Describe MLP deep networks. Why are they labeled as feedforward networks?

Answer: MLP deep networks, also known as deep feedforward networks, are the most general type of deep networks. These networks are simply large-scale neural networks that can contain many layers of neurons and handle tensors as their input. The types and characteristics of the network elements (i.e., weight functions, transfer functions) are pretty much the same as in the standard ANN models. These models are called feedforward because the flow of information that goes through them is always forwarding and no feedback connections (i.e., connections in which outputs of a model are fed back to itself) are allowed.

Diff: 2 Page Ref: 396

14 .

79) List and briefly describe two different types of computer framework used to implement deep learning. Make sure to discuss its origin hardware targets and popular implementations.

Answer: The most commonly used computer framework for deep learning implementation include: • Torch • Caffe • TensorFlow • Theano • Keras Diff: 2 Page Ref: 420-423, 370 80) What is Keras, and how is it different from the other deep learning development frameworks?

Answer: Keras (https:// keras.io/) is an open-source neural network library written in Python that functions as a high-level application programming interface (API) and is able to run on top of various deep learning frameworks including Theano and TensorFlow. In essence, Keras just by getting the key properties of network building blocks (i.e., type of layers, transfer functions, and optimizers) via an extremely simple syntax automatically generates syntax in one of the deep learning frameworks and runs that framework in the backend.

Diff: 2 Page Ref: 422-423

15 .

Business Intelligence, Analytics, Data Science, and AI, 5e (Sharda) Chapter 8 Prescriptive Analytics: Optimization and Simulation 1) Modeling can be viewed as a science in its entirety.

Answer: FALSE Diff: 2 Page Ref: 441 2) In the opening vignette, the solution helped to determine how many vendors to award contracts to, as well as the size of each of the contracts.

Answer: FALSE Diff: 2 Page Ref: 443 3) If linear programming can be successfully applied to a problem, the output is usually optimal.

Answer: TRUE Diff: 1 Page Ref: 459 4) Making optimal decisions using analytical models is what is called prescriptive analytics.

Answer: TRUE Diff: 1 Page Ref: 444 5) In order to be effective, analysts must use optimal models to solve problems without being constrained by organizational culture.

Answer: FALSE Diff: 2 Page Ref: 446 6) The use of simulation models is desirable because they can usually be solved in one pass, without incurring the time and cost of iterations.

Answer: FALSE Diff: 2 Page Ref: 475 7) An advantage of simulation is that it allows model builders to solve problems with minimal interaction with users or managers.

Answer: FALSE Diff: 2 Page Ref: 475 8) The current trend in prescriptive analytics is towards developing and using Web tools and software to access and run modeling software.

Answer: TRUE Diff: 1 Page Ref: 449 9) Using data cubes in OLAP systems opens the data up to analysis by more classes of models.

Answer: FALSE Diff: 3 Page Ref: 449 10) Another name for result variables is independent variables.

Answer: FALSE Diff: 2 Page Ref: 450 1 .

11) Taking a decision under risk is different from taking the decision under uncertainty.

Answer: TRUE Diff: 2 Page Ref: 452 12) Spreadsheets are the second most popular tool for end-user modeling.

Answer: FALSE Diff: 1 Page Ref: 454 13) Linear programming seeks to optimally allocate resources among competing activities and is likely the best-known optimization model.

Answer: TRUE Diff: 2 Page Ref: 460 14) Decision-making under uncertainty decision maker can only estimate the probability of occurrence.

Answer: FALSE Diff: 3 Page Ref: 453 15) Most managerial problems can be properly evaluated and solved using a single goal, such as profit maximization.

Answer: FALSE Diff: 3 Page Ref: 467 16) Sensitivity analysis seeks to assess the impact of changes in the input data and parameters on the proposed solution.

Answer: TRUE Diff: 2 Page Ref: 468 17) What if and goal seeking are the two most common methods of sensitivity analysis.

Answer: TRUE Diff: 2 Page Ref: 469 18) An expected value is computed by dividing the results (i.e., outcomes) by their respective probabilities and subtracting them.

Answer: FALSE Diff: 2 Page Ref: 472 19) Time compression in a simulation allows managers to assess the long-term effects of many policies.

Answer: TRUE Diff: 2 Page Ref: 475 20) Simulation modeling process starts with the construction of the detail model of the system.

Answer: FALSE Diff: 2 Page Ref: 476

2 .

21) One of the disadvantages of simulation modeling is that it usually oversimplifies the underlying real-world system or problem.

Answer: FALSE Diff: 2 Page Ref: 475 22) Genetic algorithms are a part of global search techniques used to find optimal solutions to complex problems.

Answer: FALSE Diff: 2 Page Ref: 480 23) In genetic algorithms, elitism is used to preserve a few of the best solutions to evolve through the generations.

Answer: TRUE Diff: 2 Page Ref: 482 24) Mutation in genetic algorithms refers to the task of producing new generations of potentially improved solutions by selecting parents with higher fitness ratings.

Answer: FALSE Diff: 2 Page Ref: 482 25) In linear programming formulation, decision variables describe alternative courses of actions.

Answer: TRUE Diff: 2 Page Ref: 451 26) Which of the following is not a component of decision support mathematical models?

A) Result variables B) Decision variables C) Controlled variables D) Uncontrolled variables Answer: C Diff: 3 Page Ref: 450 27) Environmental scanning is important for all of the following reasons, except?

A) Organizational culture is important and affects the model use B) It is critical to identify key corporate decision makers C) Environmental factors may have created the current problem D) Environments have greater impact on a model than the organization does Answer: D Diff: 2 Page Ref: 446

3 .

28) "Customer income" is an example of an uncontrolled variable (parameter) that would be associated with what type of decision variable?

A) Prime rate B) Audit schedule C) Tariffing level D) Advertising budget Answer: D Diff: 3 Page Ref: 451 29) Models can be built with the help of human knowledge and expertise. Another source of help in building these models is ________.

A) The customer B) Classification and clustering methods C) Customer service reps D) Business partners Answer: B Diff: 2 Page Ref: 448 30) What is an influence diagram?

A) A diagram showing the influence of decision makers B) A graphical representation of a model C) A map of the environment around decision makers D) A map of the environment around a model Answer: B Diff: 2 Page Ref: 446 31) Spreadsheets are particularly useful for all of the following reasons, except?

A) They include extensive statistical, forecasting, and other modeling and database management capabilities, functions, and routines B) They support a variety of add-ins that expand their functionality C) They can perform model solution tasks such as linear programming and regression analysis D) They require significant training to use, and hence, are usually used by experts Answer: D Diff: 3 Page Ref: 453-455 32) Linear programming belongs to a family of tools called ________.

A) Decision tree models B) Qualitative models C) Mathematical programming methods D) Heuristic programming models Answer: C Diff: 2 Page Ref: 458

4 .

33) Which of the following is not a component of a linear programming problem?

A) Internal metrics B) Constraints C) Objective function D) Decision variables Answer: A Diff: 2 Page Ref: 459 34) Because real decision-making situations involve some randomness and deal with semi structured or unstructured situations it can be said that "reality is complex." What type of decision support method is often successfully employed under these conditions?

A) Simulation B) Genetic algorithms C) Optimization D) Human intuition Answer: A Diff: 3 Page Ref: 473 35) Managers in organizations typically have ________.

A) Single goal that can be optimized using linear and nonlinear programming B) A limited number of goals that can be independently optimized using linear and nonlinear programming C) Single goal that cannot be optimized using linear and nonlinear programming D) Multiple goals that need to be simultaneously or jointly optimized Answer: D Diff: 3 Page Ref: 467 36) Sensitivity analysis is important in management support systems for all of the following reasons, except?

A) It allows flexibility and adaptation to changing conditions B) It permits the manager to input data to increase his/her confidence in the model C) It improves the mathematical optimality of the generated solutions D) It provides a better understanding of the model and the decision-making situation Answer: C Diff: 3 Page Ref: 468 37) The question "What will total earnings be if we reduce our inventory stocking costs by 10%?" is a type of ________.

A) Goal-seeking analysis B) What-if analysis C) Sensitivity analysis D) Utility modeling Answer: B Diff: 2 Page Ref: 469

5 .

38) The question "What advertising budget is needed to increase market share by 7%?" is a type of ________.

A) Goal-seeking analysis B) What-if analysis C) Sensitivity analysis D) Utility modeling Answer: A Diff: 2 Page Ref: 470 39) The question "How many servers will be needed to reduce the waiting time of restaurant customers to less than 9 minutes?" is a type of ________.

A) Goal-seeking analysis B) What-if analysis C) Sensitivity analysis D) Utility modeling Answer: A Diff: 2 Page Ref: 470 40) Decision tables are best suited to solve what types of problems?

A) Problems with a large number of alternatives B) Problems with a tabular representation C) Problems where probabilities are unknown D) Problems with a single goal Answer: D Diff: 2 Page Ref: 471 41) In handling uncertainty in decision modeling, the optimistic approach assumes ________.

A) The best possible outcome of most alternatives will occur B) The best possible outcome of some alternatives will occur C) The best possible outcome of each alternative will occur D) The best possible outcome of one alternative will occur Answer: C Diff: 3 Page Ref: 472 42) In handling uncertainty in decision modeling, what does the pessimistic approach do?

A) It assumes the worst possible outcome of one alternative will occur and then avoids it B) It assumes the worst possible outcome of some alternatives will occur and then selects the best of them C) It assumes the worst possible outcome of each alternative will occur and then selects the worst of them D) It assumes the worst possible outcome of each alternative will occur and then selects the best of them Answer: D Diff: 3 Page Ref: 472

6 .

43) An alternative representation of the decision table is a ________.

A) Gantt chart B) Fishbone diagram C) Decision tree D) Decision chart Answer: C Diff: 2 Page Ref: 471 44) Which of the following is an advantage of simulation?

A) It always results in optimal solutions B) It can incorporate significant real-life complexity C) It solves problems in one pass with no iterations D) Simulation software requires special skills Answer: B Diff: 2 Page Ref: 475 45) In which stage of the simulation methodology do you determine the variables and gather data?

A) Defining the problem B) Designing the experiment C) Testing and validating the model D) Constructing the simulation model Answer: D Diff: 3 Page Ref: 476 46) Which of the following is among the most pronounced advantages of simulation modeling?

A) Model abstraction B) Time compression C) Optimization solutions identification D) Multi-criteria decision analysis Answer: B Diff: 3 Page Ref: 475 47) Identifying a model's ________ (e.g., decision, result, uncontrollable) and their relationships is very important in creating and using models.

A) Constraints B) Objective function C) Assumptions D) Variables Answer: D Diff: 2 Page Ref: 450

7 .

48) ________ models are used to predict the future and are used widely in e-commerce.

A) Constraints B) Objective C) Optimization D) Forecasting Answer: D Diff: 2 Page Ref: 449 49) ________ modeling uses rules to determine solutions that are good enough.

A) Simulation B) Predictive C) Heuristics D) Forecasting Answer: C Diff: 2 Page Ref: 449 50) Which of the following variable types in quantitative models are controlled and determined by the decision maker?

A) Result variables B) Intermediate variables C) Decision variables D) Stochastic variables Answer: C Diff: 2 Page Ref: 450-451 51) In decision-making, fixed factors that affect the result variables but are not manipulated by decision maker are called ________ variables.

A) Result B) Intermediate C) Decision D) Uncontrollable Answer: D Diff: 2 Page Ref: 450-451 52) Deciding to purchase an FDIC-insured Certificate of Deposit at a U.S. bank can be viewed as decision making under ________.

A) Certainty B) Uncertainty C) Risk D) Stress Answer: A Diff: 2 Page Ref: 452-453

8 .

53) Simulation involves setting up a(n) ________ of a real system and conducting repetitive experiments on it.

A) Exact replica B) Opposite C) Decision tree D) Model Answer: D Diff: 2 Page Ref: 475 54) In ________ simulation, one or more of the independent variables follows certain probability distributions, which can be either discrete distributions or continuous distributions.

A) Deterministic B) Probabilistic C) Virtual D) Dynamic Answer: B Diff: 2 Page Ref: 477 55) In comparison to static models, ________ models represent behavior over time.

A) Deterministic B) Probabilistic C) Virtual D) Dynamic Answer: D Diff: 2 Page Ref: 477 56) The most common simulation method for business decision problems that deal with probability in its characterization is called ________.

A) Virtual simulation B) Variable simulation C) Monte Carlo simulation D) Deterministic simulation Answer: C Diff: 2 Page Ref: 478 57) In mathematical programming, of available, the ________ solution is the best, i.e., the degree of goal attainment associated with it is the highest.

A) Heuristic B) Optimal C) Stochastic D) Dynamic Answer: B Diff: 2 Page Ref: 459

9 .

58) This type of simulation refers to building a model of a system where the interaction between different entities is studied.

A) Heuristic B) Monte Carlo C) Discrete event D) Dynamic Answer: C Diff: 2 Page Ref: 478 59) Testing the robustness of decisions under changing conditions is an example of ________ analysis.

A) Heuristic B) Sensitivity C) Optimality D) Dynamic Answer: B Diff: 2 Page Ref: 468 60) Which of the following is not a method to handle multiple goals?

A) Utility theory B) Goal programming C) Expression of goals as constraints, using LP D) Multiple regression model Answer: D Diff: 2 Page Ref: 468 61) Which of the following methods calculates the values of the inputs necessary to achieve a desired level of output?

A) Utility theory B) Goal seeking C) What-if analysis D) Multiple regression Answer: B Diff: 3 Page Ref: 470 62) ________ conveniently organize information and knowledge in a systematic, tabular manner to prepare it for analysis and consideration of alternatives.

A) Regression models B) Decision trees C) What-if analysis D) Decision tables Answer: D Diff: 2 Page Ref: 471

10 .

63) A decision tree can be cumbersome if there are many ________ or states of nature.

A) Decisions B) Variables C) Alternatives D) Roots Answer: C Diff: 2 Page Ref: 471 64) Which of the following is among the disadvantages of simulation modeling?

A) The theory is fairly straightforward B) An optimal solution cannot be guaranteed C) The model is built from the manager's perspective D) It is often the only DSS modeling method that can handle relatively unstructured problems Answer: B Diff: 2 Page Ref: 475 65) In ________, dynamic models display systems that evolve over time, and the evolution is represented by animation.

A) Deterministic simulation B) Monte Carlo simulation C) Discrete event simulation D) Visual interactive simulation Answer: D Diff: 1 Page Ref: 479 66) Which of the following is not among the genetic algorithms' constructs?

A) Mutation B) Elitism C) Discretization D) Reproduction Answer: C Diff: 2 Page Ref: 482 67) Which of the following is not an uncontrollable variable?

A) Inflation B) Advertising budget C) Interest rate D) Tax rates Answer: B Diff: 2 Page Ref: 451

11 .

68) All of the following are among the spreadsheet add-in packages developed for DSS development, except?

A) Optimizer B) Solver C) What's Best!

D) @RISK Answer: A Diff: 2 Page Ref: 454 69) LP allocation problems usually display the following characteristics, except?

A) Unlimited quantity of economic resources available for allocation B) The resources are used in the production of products or services C) There are two or more ways in which the resources can be used D) Each activity (product or service) in which the resources are used yields a return Answer: A Diff: 2 Page Ref: 457 70) Which of the following is not true about linear programming model?

A) There can only be a single objective function B) There can be any number of decision variables or constraints C) Constraints can use "less than", "greater than", or "equal to" relationships D) Relationship among the variables can be represented as linear or nonlinear functions Answer: D Diff: 2 Page Ref: 459

12 .

71) List, briefly describe at least three advantages and at least three disadvantages of simulation modeling.

Answer: Advantages: • The theory is fairly straightforward.

• A great amount of time compression • Simulation is descriptive rather than normative • A manager can experiment to determine which decision variables • The model is built from the manager's perspective • Simulation automatically produces many important performance measures • Simulation generally can include the real complexities of problems • Simulation can handle an extremely wide variety of problem types •…

Disadvantages: • An optimal solution cannot be guaranteed, but relatively good ones are generally found.

• Simulation model construction can be a slow and costly process, although newer modeling systems are easier to use than ever.

• Solutions and inferences from a simulation study are usually not transferable to other problems because the model incorporates unique problem factors.

• Simulation is sometimes so easy to explain to managers that analytic methods are often overlooked.

• Simulation software sometimes requires special skills because of the complexity of the formal solution method.

Diff: 2 Page Ref: 475 72) List and briefly describe at least four categories of models. Give examples in each category.

Answer: • Optimization of problems with few alternatives: Find the best solution from a small number of alternatives; e.g., decision tables, decision trees, analytic hierarchy process • Optimization via algorithm: Find the best solution from a large number of alternatives, using a step-by-step improvement process; e.g., linear and other mathematical programming models, network models • Optimization via an analytic formula: Find the best solution in one step, using a formula; e.g., some inventory models • Simulation: Find a good enough solution or the best among the alternatives checked, using experimentation; e.g., Monte Carlo simulation • Heuristics: Find a good enough solution, using rules; e.g., heuristic programming, expert systems • Predictive models: Predict the future for a given scenario; e.g., forecasting models, Markov analysis Diff: 3 Page Ref: 448-449

13 .

73) All quantitative models are typically made up of four basic types of variables. List and describe them, as well as describe what links them together.

Answer: 1. Result (outcome) variables reflect the level of effectiveness of a system; that is, they indicate how well the system performs or attains its goal(s). These variables are outputs.

2. Decision variables describe alternative courses of action. The decision maker controls the decision variables.

3. Uncontrollable variables or parameters are factors that affect the result variables but are not under the control of the decision maker. Either these factors can be fixed, in which case they are called parameters, or they can vary, in which case they are called variables.

4. Intermediate result variables reflect intermediate outcomes in mathematical models. They are all linked together with mathematical relationships.

Diff: 3 Page Ref: 450-451 74) Compare and contrast decision making under uncertainty, risk and certainty.

Answer: • In decision making under certainty, it is assumed that complete knowledge is available so that the decision maker knows exactly what the outcome of each course of action will be (as in a deterministic environment).

• In decision making under uncertainty, the decision maker considers situations in which several outcomes are possible for each course of action. In contrast to the risk situation, in this case, the decision maker does not know, or cannot estimate, the probability of occurrence of the possible outcomes. Decision making under uncertainty is more difficult than decision making under certainty because there is insufficient information.

• In decision making under risk (also known as a probabilistic or stochastic decision making situation), the decision maker must consider several possible outcomes for each alternative, each with a given probability of occurrence.

Diff: 2 Page Ref: 452-453 75) List and describe the five rational economic assumptions the linear programming allocation model is based upon.

Answer: • Returns from different allocations can be compared; that is, they can be measured by a common unit (e.g., dollars, utility).

• The return from any allocation is independent of other allocations.

• The total return is the sum of the returns yielded by the different activities.

• All data are known with certainty.

• The resources are to be used in the most economical manner.

Diff: 2 Page Ref: 459

14 .

76) List and describe at least four difficulties that may arise when analyzing multiple goals.

Answer: • It is usually difficult to obtain an explicit statement of the organization's goals.

• The decision maker may change the importance assigned to specific goals over time or for different decision scenarios.

• Goals and sub-goals are viewed differently at various levels of the organization and within different departments.

• Goals change in response to changes in the organization and its environment.

• The relationship between alternatives and their role in determining goals may be difficult to quantify.

• Complex problems are solved by groups of decision makers, each of whom has a personal agenda.

• Participants assess the importance (priorities) of the various goals differently.

Diff: 2 Page Ref: 467-468 77) List and define at least four purposes sensitivity analyses are used for.

Answer: • Revising models to eliminate too-large sensitivities • Adding details about sensitive variables or scenarios • Obtaining better estimates of sensitive external variables • Altering a real-world system to reduce actual sensitivities • Accepting and using the sensitive (and hence vulnerable) real world, leading to the continuous and close monitoring of actual results Diff: 2 Page Ref: 468 78) Briefly explain Monte Carlo simulation. Comment on and exemplify the type of problems it can analyze.

Answer: Monte Carlo simulation. This method usually begins with building a model of the decision problem without having to consider the uncertainty of any variables. Then we recognize that certain parameters or variables are uncertain or follow an assumed or estimated probability distribution. This estimation is based on analysis of past data. Then we begin running sampling experiments. Running sampling experiments consists of generating random values of uncertain parameters and then computing values of the variables that are impacted by such parameters or variables.

It can be used for any business problem that has stochastic/probabilistic behavior but does not have time-variant/dynamic behavior (it is statistics).

Diff: 2 Page Ref: 478 79) List and briefly describe the six steps in the methodology of simulation.

Answer: 1. Define the problem.

2. Construct the simulation model.

3. Test and validate the model.

4. Design the experiment and conduct the experiment.

6. Evaluate the results.

7. Implement the results.

Diff: 2 Page Ref: 476 15 .

80) What are genetic algorithms used for? List and briefly describe at least four of its components/terms.

Answer: Genetic algorithms (GA) are a part of global search techniques used to find approximate solutions to optimization-type problems that are too complex to be solved with traditional optimization methods (which are guaranteed to produce the best solution to a specific problem).

• Chromosome • Reproduction • Mutation • Crossover • Elitism Diff: 2 Page Ref: 480, 482-483

16 .

Business Intelligence, Analytics, Data Science, and AI, 5e (Sharda) Chapter 9 The Landscape of Software Tools and Languages 1) Seagate, mentioned in the opening vignette, designs and manufactures data storage and management solutions.

Answer: TRUE Diff: 1 Page Ref: 495 2) With recent advancements in software technologies, the landscape of analytics tools is consolidating and contracting at an unprecedented pace.

Answer: FALSE Diff: 2 Page Ref: 497 3) The success of data scientists at work depends largely on the tool (or perhaps more accurately, the toolbox) they rely on.

Answer: TRUE Diff: 1 Page Ref: 497 4) In academia, analytics courses allocate sufficient time and coverage for the deployment of the solution/model phase.

Answer: FALSE Diff: 2 Page Ref: 498 5) Recently, we have been witnessing raising interest in commercial, proprietary analytics software tools.

Answer: FALSE Diff: 1 Page Ref: 498 6) Previously, analysts have always had access to free, but scaled- down, time limited, or capability constrained, versions of commercial analytics tools.

Answer: TRUE Diff: 1 Page Ref: 497 7) Open source refers to the software programs whose source code is open to the public to access, validate, modify, improve, and extend.

Answer: TRUE Diff: 1 Page Ref: 499 8) Nowadays, most of the proprietary tools are embracing the open-source momentum in data science by making their tools receptive and friendly to the consumption of other open source platforms, especially to R and Python.

Answer: TRUE Diff: 2 Page Ref: 499-500

1 .

9) Orange is a visual analytics tool that allows for model development locally and solution deployment on the server/cloud.

Answer: FALSE Diff: 1 Page Ref: 499-500 10) SAS Viya is a free, open source, and cloud-based analytics platform.

Answer: FALSE Diff: 1 Page Ref: 500 11) R is one of the most popular visual, workflow type analytics tools.

Answer: FALSE Diff: 1 Page Ref: 506 12) Although Google Colab is originally designed for Python programming, it can be used for R programming with some setup procedures.

Answer: TRUE Diff: 2 Page Ref: 507 13) In the R tutorial, a linkage between employee churn and salary is illustrated with a scatter plot.

Answer: FALSE Diff: 2 Page Ref: 513 14) Rattle is a popular GUI for data mining, which can be launched and used within Python environment.

Answer: FALSE Diff: 2 Page Ref: 517 15) Unlike R, Python is not just for analytics, but is a general-purpose programming language.

Answer: TRUE Diff: 1 Page Ref: 519 16) The tutorial dedicated to Python programming for analytics provides an excellent application of customer attrition, also called as churn analysis.

Answer: FALSE Diff: 2 Page Ref: 522 17) In Python, the two most popular libraries used for data ingestion, representation, and manipulation are called NumPy and Panda.

Answer: TRUE Diff: 2 Page Ref: 522 18) KNIME is a free, open-source analytics tool that also provides server extension for a fee.

Answer: TRUE Diff: 1 Page Ref: 528

2 .

19) The KNIME Hub is the public forum for the KNIME community to help each other via a number of discussion topics.

Answer: FALSE Diff: 2 Page Ref: 530 20) KNIME analytics platform can run natively on most popular operating systems such as Windows, MacOS, and Linux.

Answer: TRUE Diff: 1 Page Ref: 534 21) The tutorial provided to showcase the capabilities of Orange analytics tool was all about predicting the financial success of movies.

Answer: FALSE Diff: 2 Page Ref: 543 22) Weka is a free and open-source data mining tool originally developed and distributed by the Apache Software Foundation.

Answer: FALSE Diff: 2 Page Ref: 548 23) Rapid miner is another popular, highly capable and virtually unlimited functionality, free and open-source analytics tool.

Answer: FALSE Diff: 2 Page Ref: 550 24) Alteryx Designer, the main development tool for Alteryx analytics platform, is an easy to use, free and open-source tool that allows quick and easy creation of workflow type analytics solutions.

Answer: FALSE Diff: 2 Page Ref: 554 25) Teradata Vantage is an advanced analytics platform embedded with analytic engines and functions, which can be implemented with preferred data science languages (e.g., SQL, Python, R) and other software tools.

Answer: TRUE Diff: 1 Page Ref: 566 26) In the opening vignettes, which of the following was one the key objectives to achieve?

A) Develop the most accurate customer churn models B) Help and train employees to become citizen data scientists C) Improve the accuracy of fraud detection models D) Better forecast the customer demand for planning Answer: B Diff: 2 Page Ref: 495

3 .

27) Which of the following is used as the analogy to quick prototyping in the opening vignette in the opening vignette?

A) Solving crossword puzzles B) Solving a large sudoku puzzle C) Building objects with Lego D) Building characters with playdough Answer: C Diff: 2 Page Ref: 496 28) All of the following are expected qualities of a prolific data scientist, except?

A) Mathematical and statistical knowledge of the underlying algorithms B) Cost and efficiency analysis acumen C) Experience with the most effective analytics platforms D) Team and people management skill Answer: B Diff: 2 Page Ref: 497 29) Which of the following is not discussed in the book among the main factors to consider while selecting and employing analytics tools and platforms?

A) Ease of solution deployment B) Experimentation and quick prototyping C) Ease of use for shorter learning curve D) Business understanding and relevant data identification Answer: D Diff: 3 Page Ref: 498 30) Categorization of data analytics includes all of the following dichotomies, except?

A) Open versus closed source B) Intuitive versus complex C) Free versus commercial D) Local versus server/cloud based Answer: B Diff: 2 Page Ref: 498-500 31) Which of the following analytics tool is not among the open-source software platforms?

A) KNIME B) Weka C) PowerBI D) Python Answer: C Diff: 1 Page Ref: 500

4 .

32) All of the following analytics tools are confider server based, except?

A) Orange B) IBM Watson C) SAS Viya D) Teradata Vantage Answer: A Diff: 1 Page Ref: 500 33) According to Garner's Magic Quadrant, which of the following is among the visionaries in advanced analytics platforms?

A) IBM B) Microsoft C) SAS D) Dell Answer: B Diff: 1 Page Ref: 501 34) According to Forrester Wave for Predictive Analytics and Machine Learning Solutions, which of the following is not amongst the market leaders?

A) IBM B) KNIME C) SAS D) Microsoft Answer: D Diff: 1 Page Ref: 502 35) In the list of most popular analytics tools (according to a poll administered by KDnuggest.com), which of the following is not among the categories used to categorize the tools?

A) Local/server based B) Free/open source C) Commercial/proprietary D) Bid data technologies Answer: A Diff: 2 Page Ref: 503 36) What is the main reason for Python and R not being listed as among the leaders in both Gartner and Forrester analytics market reports?

A) They are not as good as the ones listed therein B) They do not provide analytics solution in a comprehensive manner C) They are not company products D) They are not open-source analytics tools Answer: C Diff: 2 Page Ref: 502

5 .

37) Which of the following is not true about R analytics tool?

A) It is a free tool that comes with a rich collection of functions and libraries B) It is a high-level programming language C) It is a server-based analytics platform D) It is open source Answer: C Diff: 2 Page Ref: 500 38) The function and procedure that comes with R can be written in which of the following programming languages?

A) FORTRAN B) C++ C) R D) All of the other answers are true Answer: D Diff: 2 Page Ref: 506 39) Which of the following is not true about R? A) Most of the user-visible functions in R are written in R B) It is one of the most popular analytics tools C) It is a compiled computer language D) It is commonly known as a system for statistical computation and beautiful graphics Answer: C Diff: 3 Page Ref: 506 40) All of the following are common ways to acquire and use R, except?

A) Anaconda B) Visual Studio Code C) Google Colab D) Spyder Answer: D Diff: 2 Page Ref: 506-507 41) Which of the following analytics related tasks R is not a good tool to use?

A) Descriptive statistics B) Data visualization C) Machine learning D) Project management Answer: D Diff: 1 Page Ref: 506

6 .

42) ________ is a visual data mining tool that can be instantiated, launched, and used from within R environment.

A) Weka B) Rattle C) Orange D) JMP Answer: B Diff: 2 Page Ref: 517 43) Python can be used for all of the following, except?

A) Determination of organizational KPIs B) Building websites C) Developing specializes software D) Automate data acquisition Answer: A Diff: 2 Page Ref: 519 44) Which of the following statements is not true about Python?

A) It is a dynamic, interpreted computer programming language B) In addition to analytics, it can also be used to create a wide variety of computer applications C) It does not require type declarations of variables or parameters D) It is freely distributed by the Apache Software Foundation Answer: D Diff: 2 Page Ref: 520 45) Which of the following is not among the popular IDE for python programming?

A) Jupiter notebook B) Spyder C) JMP Pro D) Visual Studio Code Answer: C Diff: 2 Page Ref: 521 46) ________ was originally developed by Google and designed to make Python programming easy for teams on a server environment.

A) Jupiter notebook B) Colab C) Spyder D) PyCharm Answer: B Diff: 2 Page Ref: 521

7 .

47) Which of the following libraries are commonly used for data ingestion and manipulation while programming in Python?

A) Import B) Ingest C) Panda D) Catch Answer: C Diff: 2 Page Ref: 522 48) In Python, the most popular library used for machine learning is called ________.

A) Statquest B) Sklearn C) Maclearn D) Traintest Answer: B Diff: 2 Page Ref: 524 49) Popular open-source visual analytics tools include all of the following, except?

A) Python B) Orange C) KNIME D) RapidMiner Answer: A Diff: 2 Page Ref: 524 50) ________ is a visual analytics tool originally developed to be used in pharmaceutical analytics research and then quickly evolved into a generic data science platform.

A) Python B) Orange C) KNIME D) RapidMiner Answer: C Diff: 2 Page Ref: 524 51) Which of the following popular data science tools cannot be incorporated in KNIME (Konstanz Information Miner) analytics projects?

A) Spark B) H2O C) Python D) RapidMiner Answer: D Diff: 2 Page Ref: 529

8 .

52) Which of the following is a good definition for workflow as it relates to visual analytics tools and platforms?

A) It is a canvas for high level analytics coding B) It is a pull-down menu driven model analytics development process C) It is a graphical representation of the logical and data flow D) It is a logical representation of the objective function and constraints Answer: C Diff: 2 Page Ref: 529 53) In KNIME analytics platform, workflow coach is used to ________.

A) Develop automated analytics models B) Collaborate with the expert in the community C) Access node recommendation D) Access the description of a specific node in the workflow editor Answer: C Diff: 2 Page Ref: 530 54) ________ is the public repository for the KNIME community where users share nodes, components, and workflows with others.

A) KNIME Forum B) KNIME Workflow Coach C) KNIME Node Repository D) KNME Hub Answer: D Diff: 2 Page Ref: 530-531 55) ________ has become very popular in recent times; it promises to get the data and spit out some results, without user lifting a finger.

A) Machine learning B) AutoML C) Explainable AI D) Workflow automation Answer: B Diff: 2 Page Ref: 532 56) In KNIME ecosystem, ________ make the transition from design and development to productizing and deployment an easy and quick process.

A) KNIME Hub B) KNIME Workflow Designer C) KNIME Server D) KNIME Workflow Coach Answer: C Diff: 2 Page Ref: 534

9 .

57) ________ is one of the most admired characteristics of KNIME analytics platform that allows for building your own nodes.

A) Explainability B) Connectivity C) Modularity D) Expandability Answer: D Diff: 2 Page Ref: 534 58) Following are among the options one can execute to ingest/read the input data files into KNIME, except?

A) All of the other answers are true B) Use the proper data reading node within the workflow designer C) Drug-and drop a data file from file directly into the workflow designer D) Drug and drop a data file from within KNIME explorer into the workflow designer Answer: A Diff: 2 Page Ref: 536 59) Approximately now many nodes/functions come with the initial install of the KNIME Analytics Platform?

A) Countless B) About 500 C) Over 2000 D) Between 100 and 500 Answer: C Diff: 1 Page Ref: 534 60) In customer churn prediction tutorial for KNIME, what type of data splitting ratio is used?

A) 10-fold cross validation B) 50/50 split C) 70/30 split D) 80/20 split Answer: C Diff: 2 Page Ref: 539 61) ________ is a free, python-based, open-source software developed by a group of academics at the University of Ljubljana.

A) Weka B) KNIME C) JMP D) Orange Answer: D Diff: 1 Page Ref: 543

10 .

62) What type of application is used to showcase the functionality of Orange analytics tool?

A) Customer churn analysis B) Text mining of published literature C) Sentiment analysis of social media posting D) Predicting and explaining fraudulent transactions Answer: B Diff: 2 Page Ref: 543 63) Which of the following is not among the reporting outputs of the Orange tutorial?

A) Prediction accuracies B) Topic-term visualizations C) Trend analysis D) List of topic and terms Answer: A Diff: 2 Page Ref: 543 64) All of the following topic modeling techniques are used in the Orange tutorial, except?

A) LSA B) PCA C) LDA D) HDP Answer: B Diff: 3 Page Ref: 544 65) ________, developed at the University of Waikato, is a free and open-source software tool for data mining.

A) KNIME B) Orange C) Weka D) RapidMiner Answer: C Diff: 3 Page Ref: 548 66) Which of the following is not among the data mining tasks supported by Weka?

A) Data visualization B) Descriptive statistics C) Classification D) Text mining Answer: D Diff: 2 Page Ref: 548

11 .

67) All of the following are among the options one can use to develop data mining models in Weka, except?

A) Jupiter Notebook B) Explorer C) Simple CLI D) Knowledge Flow Answer: A Diff: 2 Page Ref: 549 68) Which of the following is not true about RapidMiner analytics software?

A) It is completely free and open source B) It is origin is called YALE (yet another learning environment) C) It employs a visual workflow design interface D) It is developed by a group of researchers in one of the universities in Europe Answer: A Diff: 2 Page Ref: 550 69) Which of the following is not among the popular commercial analytics tools?

A) JMP B) RapidMiner C) Alteryx D) Orange Answer: D Diff: 2 Page Ref: 553 70) The advantages SAS Viya over the traditional analytics tools includes all of the following, except?

A) Scalability B) Execution speed C) Ease of use D) Open source Answer: D Diff: 2 Page Ref: 559-559 71) Which of the following is not a true statement about JMP Pro analytics software?

A) It is owned by SAS B) It can perform statistics, data mining, as well as text mining tasks C) It employs a visual workflow type user interface D) It is a commercial and proprietary tool Answer: C Diff: 2 Page Ref: 560

12 .

72) The success of data scientists at work depends largely on what? List and define at least four factors.

Answer: The mathematical and statistical knowledge of the underlying algorithms The experience with the most effective analytics platforms The domain knowledge/wisdom Team/people management skills Diff: 2 Page Ref: 497 73) In analytics, what is deployment? Why aren't academic programs covering it in due details?

Answer: Deployment is where the "perfect" solution that we built is put in use so that it could make a difference in the intended parts of the organization. Traditionally, in academia, in our analytics courses, we don't allocate enough time and effort to cover this phase in due details.

This lack of coverage may be attributed to the fact that every system has its own specific technical structure, deployment procedures, integration mechanisms, and security policies.

Diff: 2 Page Ref: 498 74) List and briefly describe the four categories used to discern analytics tools.

Answer: 1. Free versus Paid/Commercial 2. Open versus Closed Source 3. Local versus Server/Cloud 4. Visual versus Syntactic Diff: 2 Page Ref: 498-499 75) Identify four free/open source and four commercial/proprietary analytics tools. Which tool is your favorite? Why? Answer: Free/Open Source • Python •R • KNIME • Orange • Weka Commercial/Proprietary • Tableau • PowerBI • SAS Viya • SAS EM • JMP • IBM Modeler • IBM Watson • TIBCO Statistica • Teradata Vantage My favorite is KNIME, because it is truly free, open-source, and highly functional.

Diff: 1 Page Ref: 500

13 .

76) What is R? What is it good for? How do you get started with R (list at least four options)?

Answer: R is one of the most popular, high-level programming-based analytics tools. It is commonly known as a system for statistical computation and stunning graphics.

You can get started with R using one of the following options: • R-Project • RStudio (standalone or cloud) • Anaconda • Visual Studio Code • Jupiter Notebook • Google Colab Diff: 2 Page Ref: 505-507 77) What is Rattle? How do you install and use it?

Answer: Rattle is a GUI-based data mining tool which can be installed and launched from within R. It presents statistical description and visual summaries of data, transforms data so that it can be readily for modeling, builds both unsupervised and supervised machine learning models.

It can be installed from Togaware on all three operating systems: Linux, Windows, Mac. Diff: 2 Page Ref: 517-518 78) What is Python? List and briefly describe at least four computational tasks that Python can be used.

Answer: Python is one of the most popular analytics and computer programming languages.

It can be used for: (1) building websites and ecommerce applications, (2) developing specializes software for a variety of business applications, (3) automate computational tasks, (4) automate data acquisition and storage, and (5) conduct data analytics (e.g., business intelligence, business analytics, data science, data mining, machine learning, deep learning, among others).

Diff: 2 Page Ref: 519

14 .

79) What is KNIME? From the top ten reasons to consider KNIME as an analytics tool, pick and justify your selection of the top three.

Answer: KNIME (Konstanz Information Miner) is a free and open-source data analytics, visualization, reporting, and data bending/integration software platform.

Top 10 reasons to consider KNIME: 1. Easy to Use 2. Open Source 3. Cost - it is truly free!

4. Community support 5. Rich functionality with over 2000 native nodes 6. Platform agnostic — runs natively on Windows, Mac and Linux 7. Connectivity to most every data source 8. Expandability by creating your own nodes 9. Modularity to create reusable modeling artifacts at varying levels of granularity via the use of meta-nodes and components 10. Deployability via KNIME server Diff: 2 Page Ref: 528, 534 80) List and briefly describe the four-step process used to mine published literature in Orange tutorial.

Answer: Step 1. Gathering and cleaning the textual data Step 2. Preprocessing the textual data Step 3. Performing the text analysis Step 4. Reporting of the obtained results Diff: 2 Page Ref: 544-547

15 .

81) What is Alteryx Designer? Briefly compare Alteryx Designer to Excel, Tableau, and KNIME. Answer: Alteryx Designer, the main development tool for Alteryx analytics platform, is an easy to use, drag-and-drop user interface that allows quick and easy creation of workflow type analytics solutions to business problems.

Comparison to Excel, Tableau, and KNIME: Excel is a general-purpose spreadsheet modeling and data manipulation tool (not really a business intelligence and data analytics tool) Tableau is primarily a data visualization tool.

Both Excel and Tableau provide some advanced analytics capabilities via add-ons and functional integrations/scripting, compared to Alteryx, they are not full-fledged business analytics and data science tools.

Perhaps the most appropriate, all-inclusive, head-to-head comparison of Alteryx can be made against KNIME. They are very similar in broad data ingestion and manipulation functionalities, appealing workflow-type drug-and-drop model development process, utilization of other tools and programming languages, and seamless cloud-based deployment options. The main difference are as follows: 1. Alteryx is a commercial and proprietary tool while KNIME is a free and open-source platform, 2. Alteryx Designer runs only on Windows platform while KNIME Analytics Platform runs natively on Windows, Mac, and Linux, 3. Alteryx provides a very limited version of their tool to educational institutions (including only the data preprocessing and visualization function, not including any of the key functions like machine learning, text mining, computer vision), while KNIME provides all of its functions without any limitations to educational institutions and commercial users, and 4. Alteryx is designed to have fewer building blocks (for ease of model development by nontechnical users) and hence has a shorter learning curve, while KNIME has thousands of nodes for any low-level data manipulation and model building tasks, making it suitable to more technical users with slightly longer learning curve.

Diff: 3 Page Ref: 555-556

16 .

Business Intelligence, Analytics, Data Science, and AI, 5e (Sharda) Chapter 10 New AI-Based Trends in Analytics and Data Science 1) In the opening vignette, Discover Foods chooses linear regression over LSTM because the performance of linear regression was on par with the LSTM type deep learning model.

Answer: FALSE Diff: 2 Page Ref: 581 2) As stated in the opening vignette, because food quality and safety can be compromised, extra emphasis must be given to accurate prediction of high temperature outliers.

Answer: TRUE Diff: 2 Page Ref: 581 3) Cloud computing is a style of computing in which dynamically scalable and often virtualized resources are provided over the Internet.

Answer: TRUE Diff: 1 Page Ref: 581 4) The term cloud in cloud computing refers to the large computing infrastructure accessed via a local area network.

Answer: FALSE Diff: 2 Page Ref: 583 5) In cloud computing, SaaS stands for scalable analytics application services.

Answer: FALSE Diff: 1 Page Ref: 583 6) Web-based e-mail can be considered a basic cloud application.

Answer: TRUE Diff: 1 Page Ref: 583 7) The concept of data as a service advocates the view that where data lives (the actual platform on which the data resides) does not matter.

Answer: TRUE Diff: 1 Page Ref: 584 8) Desktop as a Service providers offer virtual desktop with all the required applications for the end users to perform their tasks without having to worry about the software, hardware, and security of the desktop.

Answer: TRUE Diff: 1 Page Ref: 585 9) Infrastructure as a Service (IaaS) allows customers to use applications and software that run on distant computers in the cloud infrastructure.

Answer: FALSE Diff: 2 Page Ref: 585 1 .

10) Examples of PaaS provides include Microsoft (Azure), Amazon (EC2), and Google (App Engine).

Answer: TRUE Diff: 1 Page Ref: 585 11) Virtualization in cloud computing is the creation of an exact local replica of a computational resource such as an operating system.

Answer: FALSE Diff: 2 Page Ref: 586 12) Storage virtualization is the splitting of available bandwidth into channels, which disguises complexity of the network by dividing it into manageable parts.

Answer: FALSE Diff: 2 Page Ref: 586 13) With public cloud, the subscriber uses the shared resources offered by service providers over the Internet.

Answer: TRUE Diff: 1 Page Ref: 589 14) OpenShift is Google's cloud application platform based on a PaaS model.

Answer: TRUE Diff: 1 Page Ref: 590 15) Amazon QuickSight is a cloud-based business intelligence tool that helps to analyze, create customized visualizations/dashboards, and get insights from our data by seamlessly integrating with data sources.

Answer: TRUE Diff: 1 Page Ref: 591 16) Snowflake is one of IBM's latest cloud-based data analytics offerings.

Answer: FALSE Diff: 2 Page Ref: 591 17) Data-oriented cloud systems are immune to some of the issues that local systems are usually subject to such as data security and governance.

Answer: FALSE Diff: 2 Page Ref: 593 18) According to location-based analytics taxonomy, examining geographic site locations is a kind of consumer-oriented analytics.

Answer: FALSE Diff: 2 Page Ref: 594

2 .

19) Geospatial analysis can enable communication companies to capture daily transactions from a network to identify the geographic areas experiencing many failed connection attempts of voice, data, text, or Internet.

Answer: TRUE Diff: 1 Page Ref: 598 20) Location-based analytics often finds its application in consumer-oriented marketing efforts.

Answer: TRUE Diff: 1 Page Ref: 599 21) ParkPGH, is a location-based analytics app developed for tourists to find recreational parks within big cities.

Answer: FALSE Diff: 2 Page Ref: 600 22) Imazon, an image analytics platform, which is using satellite data to develop real-time information on Amazon deforestation.

Answer: TRUE Diff: 1 Page Ref: 601 23) Analysis of surveillance images coupled with facial recognition technologies help track all subjects of interest without jeopardizing privacy.

Answer: FALSE Diff: 2 Page Ref: 602 24) IoT is not an application; it is an infrastructure, platform, or framework that is used to support applications.

Answer: TRUE Diff: 1 Page Ref: 609 25) The most significant factor that defines Web 3.0 is that its fundamental building block is the open blockchain technology.

Answer: TRUE Diff: 1 Page Ref: 627 26) In the opening vignette, which of the following machine learning methods is not used for the prediction models for food safety?

A) Support vector machines (SVM) B) Long-short term memory (LSTM) C) K-nearest neighbor (KNN) D) Naïve Bayes (NB) Answer: D Diff: 2 Page Ref: 580-581

3 .

27) In the opening vignette, in the end, which model type produced the best prediction, especially for outliers?

A) Support vector machines (SVM) B) Long-short term memory (LSTM) C) K-nearest neighbor (KNN) D) Naïve Bayes (NB) Answer: B Diff: 2 Page Ref: 581-583 28) ________ is a style of computing in which dynamically scalable and often virtualized resources are provided over the Internet.

A) Edge computing B) Cloud computing C) Quantum computing D) Web-based computing Answer: B Diff: 1 Page Ref: 581 29) Which of the following is not among the trends from where cloud computing is believed to be originated?

A) Application service provider B) Software as a service C) Quantum computing D) Utility computing Answer: C Diff: 2 Page Ref: 581 30) In cloud computing, what does "cloud" usually refer to?

A) Meteorological studies B) Deep learning C) Data warehouse D) Internet Answer: D Diff: 2 Page Ref: 583 31) Several companies offer cloud-computing services; which of the following is not one of them?

A) Microsoft B) Adobe C) Salesforce D) Gartner Answer: D Diff: 2 Page Ref: 583

4 .

32) In service-oriented decision support solutions, which of the following cannot be obtained individually or bundled and provided to the users as a service?

A) End-user components B) Online analytic processing C) Online transaction processing D) Data warehouses Answer: C Diff: 2 Page Ref: 583 33) With ________ any business process can access data wherever it resides.

A) Information as a service B) Infrastructure as a service C) Data as a service D) Integration as a service Answer: C Diff: 2 Page Ref: 584 34) Which of the following open data standard is not among the ones often used for enabling applications to access the data?

A) NoSQL B) SQL C) XML D) XQuery Answer: A Diff: 2 Page Ref: 585 35) The advantages of DaaS includes all of the following, except?

A) Improve data quality because there is a single point for updates B) Simplicity of the data access C) Integration and processing of large dataset is a breeze (efficiency) D) Reimplementation is easy because the changes are minimal (agility) Answer: C Diff: 2 Page Ref: 585 36) Which of the following is a prime example for Desktop as a Service?

A) Microsoft Azure B) Amazon WorkSpace C) Google Cloud D) IBM DeepBlue Answer: B Diff: 2 Page Ref: 585

5 .

37) This model allows consumers to use applications and software that run on distant computers in the cloud infrastructure.

A) PaaS B) IaaS C) SaaS D) DaaS Answer: C Diff: 2 Page Ref: 585 38) Which of the following cloud offering provides the broadest technology stack that is managed by the cloud vendor?

A) Platform as a Service (PaaS) B) Infrastructure as a Service (IaaS) C) Software as a Service (SaaS) D) Data as a Service (DaaS) Answer: C Diff: 3 Page Ref: 586 39) Which of the following is not among the commonly used virtualization types?

A) Server virtualization B) Network virtualization C) Storage virtualization D) Data virtualization Answer: D Diff: 3 Page Ref: 586 40) It is a type of virtualization where available bandwidth is split into channels, which disguises complexity of the network by dividing it into manageable parts.

A) Server virtualization B) Network virtualization C) Storage virtualization D) Data virtualization Answer: B Diff: 3 Page Ref: 586 41) Which of the following is not among the most common models used for cloud development?

A) Virtual cloud B) Public cloud C) Hybrid cloud D) Private cloud Answer: A Diff: 3 Page Ref: 589

6 .

42) This type of cloud model is often adopted and used by organizations that have a mission critical workload and security concerns.

A) Virtual cloud B) Public cloud C) Hybrid cloud D) Private cloud Answer: D Diff: 1 Page Ref: 589 43) Amazon Elastic Beanstalk is a service offered by Amazon Web Services that can deploy, manage, and scale Web applications. Which of the following programming languages is not supported by this platform?

A) C++ B) Java C) Ruby D) PHP Answer: A Diff: 3 Page Ref: 589 44) Which of the following is not among the major cloud platform providers in app development and deployment space?

A) Microsoft B) Oracle C) Google D) IBM Answer: B Diff: 2 Page Ref: 589-590 45) ________ is Red Hat's cloud application platform based on a PaaS model.

A) Beanstalk B) OpenAI C) OpenShift D) AppEngine Answer: C Diff: 2 Page Ref: 590 46) Which of the following combines aspects of cloud computing with Big Data analytics and empowers data scientists and analysts to access centrally managed data and algorithms?

A) Platform as a Service (PaaS) B) Infrastructure as a Service (IaaS) C) Analytics as a Service (SaaS) D) Data as a Service (DaaS) Answer: C Diff: 2 Page Ref: 590

7 .

47) Which of the following is not among the promising applications of analytics as a service (AaaS)?

A) Multi-faceted gene sequencing B) Large-scale optimization C) Complex multicriteria decision problems D) Data and text mining Answer: A Diff: 2 Page Ref: 591 48) ________ is a cloud-based business intelligence tool offered by Amazon which helps end users to analyze and create customized visualizations.

A) Amazon AWS B) Amazon Beanstalk C) Amazon OpenShift D) Amazon QuichSight Answer: D Diff: 2 Page Ref: 591 49) ________ is an attempt by SAS Institute to make its analytics software offering available on demand through the cloud.

A) SAS Web Studio B) SAS Viya C) SAS Enterprise Miner D) SAS Everywhere Answer: B Diff: 2 Page Ref: 591 50) ________ is a cloud-based data warehouse solution where users can bring together their data from multiple sources as one source to analyze.

A) Tableau B) PowerBI C) SAS Viya D) Snowflake Answer: D Diff: 2 Page Ref: 591 51) Which of the following is not among the concerns raised about cloud computing?

A) Loss of control and privacy B) Intrusion susceptibility C) Legal liabilities D) Cross-border political issues Answer: B Diff: 2 Page Ref: 593

8 .

52) According to Cloud Security Alliance, which of the following is not among the top security threats in the cloud?

A) Insecure interface B) Loss of control and privacy C) Hardware failure of equipment D) Data loss and leakage Answer: B Diff: 2 Page Ref: 593 53) Which of the following is the major advantage of cloud analytics?

A) Rapid diffusion of advanced analysis tools among the users B) Enhanced control over privacy C) Prevention of hardware failure D) Avoidance of data loss and leakage Answer: A Diff: 2 Page Ref: 593 54) Location-based data for analytics are nowadays readily available from ________.

A) Geographic information systems B) RFID systems C) High-level sensor networks D) Commercial data banks Answer: A Diff: 2 Page Ref: 595 55) Which of the following is not among the popular GIS-based location analytics application areas?

A) Disease spread prediction B) Agricultural analytics C) Crime analytics D) Customer churn analysis Answer: D Diff: 2 Page Ref: 595 56) The ability to analyze organizational data enhanced with location-specific granularity helps communication companies to realize all of the following benefits, except?

A) Increasing operational efficiency B) Enhanced quality of service C) Increased revenue D) Fewer privacy issues Answer: D Diff: 2 Page Ref: 598

9 .

57) Which of the following is not one of the technologies that modern consumer devices use to transmit their locations information to be used in real-time location intelligence?

A) Cell tower triangulation B) GIS C) GPS D) Wi-Fi Answer: B Diff: 3 Page Ref: 599 58) ________ technologies depend upon the location information, images through camera, as well as other sensors (when available) to provide the user an immersive in-presence experience.

A) Enhanced reality B) Augmented reality C) Sensible reality D) Dual reality Answer: B Diff: 2 Page Ref: 599 59) What type of assistance does consumer analytics application Waze provide?

A) Help finding a parking spot in big cities B) Help with international food recipes C) Help with traffic navigation and road conditions D) Help with finding and sending occasion specific flower buckets Answer: C Diff: 2 Page Ref: 600 60) One of the biggest concerns with the use of consumers analytics applications is ________.

A) Safety B) Privacy C) Isolation D) Loss of time Answer: B Diff: 1 Page Ref: 600 61) Which of the following is not among the advanced that help in popularization of consumer facing image analytics applications?

A) Edge computing B) High-resolution cameras C) Deep learning D) Storage capacities Answer: A Diff: 2 Page Ref: 601

10 .

62) Multispectral, high-resolution satellite data helps scientists to do all of the following, except?

A) Assess global change/warming B) Monitor weather C) Locate person of interests D) Track land usage Answer: C Diff: 1 Page Ref: 601 63) ________ refers to a computerized network that connects many objects (people, animals, devices, sensors, buildings, items) each with an embedded microprocessor.

A) IoT B) Wi-Fi C) GIS D) GPU Answer: A Diff: 1 Page Ref: 604 64) All of the following are correct statements about IoT, except?

A) Large numbers of objects (things) can be connected B) Each thing communicates over the local area network (LAN) C) Each thing has a unique definition (IP address) D) Each thing has the ability to receive, send, and store data automatically Answer: B Diff: 3 Page Ref: 605 65) All of the following are the highest level of categories in defining IoT ecosystem, except?

A) Software Providers B) Platforms & Enablement (Horizontals) C) Applications (Verticals) D) Building Blocks Answer: A Diff: 2 Page Ref: 606 66) All of the following are among the major IoT technology blocks, except?

A) Hardware B) Service Providers C) Connectivity D) Applications Answer: B Diff: 1 Page Ref: 607-608

11 .

67) Which of the following is not among the barriers to adoption of smart homes?

A) Collaboration B) Compatibility C) Communication D) Concentration Answer: A Diff: 3 Page Ref: 616 68) All of the following are the issues raised about implementation of autonomous cars, except?

A) Lack of smart roads and infrastructure B) Transmission delays in IoT connectivity C) Old habits die hard — cultural barriers D) Cost of real-time 3D map technologies Answer: A Diff: 2 Page Ref: 618 69) Which of the following is not among the most acclaimed advantages of 5G technologies?

A) It provides high-speed access B) It delivers significant improvements to security and privacy, and (v) C) It provides increased flexibility via a service-based architecture D) It helps with real-time support if managerial decisioning Answer: D Diff: 2 Page Ref: 618 70) ________ enables performing of as much of the computation/analytics at the outer extend of the network rather than back in a central cloud.

A) Quantum computing B) Edge computing C) Massively parallel processing (MPP) D) In-database analytics Answer: B Diff: 2 Page Ref: 619 71) A new technology which reduces the workloads of the employees by automating the repetitive, high-volume steps in processes is called ________.

A) Augmented reality (AR) B) Virtual reality (VR) C) Robotic process automation (RPA) D) Massively parallel processing (MPP) Answer: C Diff: 2 Page Ref: 621

12 .

72) List and briefly describe the four major components of service-oriented decision support solutions.

Answer: Major components of service-oriented decision support solutions include (1) operational systems (2) data warehouses (3) online analytic processing, and (4) end-user components Diff: 2 Page Ref: 584 73) List and briefly describe at least four "as-a-service" types of business analytics service offerings.

Answer: Analytics as a Service (AaaS) Data as a Service (DaaS) Desktop as a Service (DaaS) Software as a Service (SaaS) Platform as a Service (PaaS) Infrastructure as a Service (IaaS) Diff: 2 Page Ref: 584-585 74) List and define the three main virtualization types.

Answer: 1) Network virtualization: It is the splitting of available bandwidth into channels, which disguises complexity of the network by dividing it into manageable parts. Then each bandwidth can be allocated to a particular server or device in real time.

2) Storage virtualization: It is the pooling of physical storage from multiple network storage devices into a single storage device that can be managed from a central console.

3) Server virtualization: It is the masking of physical servers from server users. Users do not have to manage the actual servers or understand complicated details of server resources.

Diff: 2 Page Ref: 586-587

13 .

75) List and briefly define the three major cloud development options/models.

Answer: Private cloud: This can also be called internal cloud or corporate cloud. It is a more secure form of cloud service than public clouds like Microsoft Azure and Amazon Web Services.

It is operated solely for a single organization having a mission critical workload and security concerns. It provides the same benefits as a public cloud-like service, scalability, changing computing resources on demand, and so on. Companies that have a private cloud have direct control over their data and applications. The disadvantage of having a private cloud is the cost of maintaining and managing the cloud because on-premises IT staff are responsible for managing it.

Public cloud: In this model the subscriber uses the resources offered by service providers over the Internet. The cloud infrastructure is managed by the service provider. The main advantage of this public cloud model is saving time and money in setting up hardware and software required to run their business. Examples of public clouds are Microsoft Azure, Google Cloud Platform, and Amazon AWS. Hybrid cloud: The hybrid cloud gives businesses great flexibility by moving workloads between private and public clouds. For example, a company can use hybrid cloud storage to store its sales and marketing data, and then use a public cloud platform like Amazon Redshift to run analytical queries to analyze its data. The main requirement is network connectivity and API (application program interface) compatibility between the private and public cloud.

Diff: 2 Page Ref: 589 76) Provide the classification (or taxonomy) of location-based analytic applications. Provide an example to each of the classes.

Answer: • LOCATION-BASED ANALYTICS ⸰ ORGANIZATION ORIENTED ▪ GEOSPATIAL STATIC APPROACH (e.g., Examining Geographic SiteLocations) ▪ LOCATION-BASED DYNAMIC APPROACH (e.g., Live Location Feeds; Real-Time Marketing Promotions) ⸰ CONSUMER ORIENTED ▪ GEOSPATIAL STATIC APPROACH (e.g., GPS Navigation and Data Analysis) ▪ LOCATION-BASED DYNAMIC APPROACH (e.g., Historic and Current Location Demand Analysis; Predictive Parking; Health-Social Networks) Diff: 3 Page Ref: 594-595 77) Define IoT. List the five characteristics of general IoT applications.

Answer: IoT refers to a computerized network that connects many objects (people, animals, devices, sensors, buildings, items) each with an embedded microprocessor.

Common characteristics of IoT applications: • Large numbers of objects (things) can be connected.

• Each thing has a unique definition (IP address).

• Each thing has the ability to receive, send, and store data automatically.

• Each thing is delivered mostly over the wireless Internet.

• Each thing is built upon machine-to-machine (M2M) communication.

Diff: 3 Page Ref: 604-605

14 .

78) List and briefly describe the four major blocks of IoT technology.

Answer: 1. Hardware: This includes the physical devices, sensors, and actuators where data are produced and recorded. The devices are the equipment that needs to be controlled, monitored, or tracked.

IoT sensor devices could contain a processor or any computing device that parses incoming data.

2. Connectivity: There should be a base station or hub that collects data from the sensor-laden objects and sends those data to the "cloud" to be analyzed. Devices are connected to a network to communicate with other networks or other applications. These may be directly connected to the Internet. A gateway enables devices that are not directly connected to the Internet to reach the cloud platform.

3. Software backend: In this layer, the data collected are managed. Software backend manages connected networks and devices and provides data integration. This may very well be in the cloud.

4. Applications: In this part of IoT, data are turned into meaningful information. Many of the applications can run on smartphones, tablets, and PCs and do something useful with the data.

Other applications can run on the server and provide results or alerts through dashboards or messages to the stakeholders.

Diff: 2 Page Ref: 607-608 79) List and describe at least five major benefits of IoT. Answer: • Reduces cost by automating processes.

• Improves workers' productivity.

• Creates new revenue streams.

• Optimizes asset utilization (e.g., see the opening vignette).

• Improves sustainability.

• Changes and improves everything.

• May anticipate our needs (predictions).

• Enables insights into broad environments (sensors collect data).

• Enables smarter decisions/purchases.

• Provides increased accuracy of predictions.

• Identifies problems quickly (even before they occur).

• Provides instant information generation and dissemination.

• Offers quick and inexpensive tracking of activities.

• Makes business processes more efficient.

• Enables communication between consumers and financial institutions.

• Facilitates growth strategy.

• Fundamentally improves the use of analytics (see the opening vignette).

• Enables better decision making based on real-time information.

• Expedites problem resolution and malfunction recovery.

• Supports facility integration.

• Provides better knowledge about customers for personalized services and marketing.

Diff: 2 Page Ref: 608

15 .

80) List and describe at least five major drivers of IoT. Answer: • The number of "things"–20 to 50 billion–may be connected to the Internet by 2020—2025.

• Connected autonomous "things"/systems (e.g., robots, cars) create new IoT applications.

• Broadband Internet is more widely available, increasing with time.

• The cost of devices and sensors is continuously declining.

• The cost of connecting the devices is decreasing.

• Additional devices are created (via innovations) and are interconnected easily.

• More sensors are built into devices.

• Smartphones' penetration is skyrocketing.

• The availability of wearable devices is increasing.

• The speed of moving data is increasing to 60 THz. • Protocols are developing for IoT (e.g., WiGig).

• Customer expectations are rising; innovative customer services are becoming a necessity.

• The availability of IoT tools and platforms is increasing.

• The availability of powerful analytics that are used with IoT is increasing.

Diff: 2 Page Ref: 608-609 81) What is 5G and how does it relate to Edge Computing? List at least three key benefits of 5G technology.

Answer: Fifth-generation (5G) is the new mobile technology, which provides superior speeds and ultra-low latencies to enable diverse set of breakthrough analytics use cases.

Edge computing, which allows for performing as much of the computation/analytics at the edge rather than back in a central cloud, is an enabler of 5G technologies.

Advantaged of 5G include: (i) it provides very high-speed access, even in densely populated areas, (ii) it connects everything, thereby supporting massive IoT and machine- to-machine (M2M) communications, (iii) it provides real-time latency, thereby minimizing delays in network response time and enabling entirely new services and applications for vertical industries, (iv) it delivers significant improvements to security and privacy, and (v) it provides a service-based architecture (SBA) that delivers increased flexibility and service diversity.

Diff: 3 Page Ref: 619

16 .

Business Intelligence, Analytics, Data Science, and AI, 5e (Sharda) Chapter 11 Ethical, Privacy and Managerial Considerations in Analytics 1) Business problems are the problems where the research teams are willing to learn some new things from the data and are often required to innovate a new method to solve these kinds of problems.

Answer: FALSE Diff: 2 Page Ref: 634 2) According to the opening vignette, simple predictive models can be better explained, and you can define the relationship in terms of some mathematical formula.

Answer: TRUE Diff: 2 Page Ref: 635 3) Once the models are developed, implementing business analytic systems is not a complex undertaking.

Answer: FALSE Diff: 2 Page Ref: 637 4) The implementation process of intelligent systems is like the generic process of other information systems.

Answer: TRUE Diff: 2 Page Ref: 637 5) In designing analytic systems, it must be kept in mind that the right to an individual's privacy is not absolute.

Answer: TRUE Diff: 2 Page Ref: 646 6) The public's right to know is not superior to the individual's right to privacy.

Answer: FALSE Diff: 2 Page Ref: 646 7) Involvement of senior executives in analytics projects typically slows down the project and leads to failure.

Answer: FALSE Diff: 2 Page Ref: 638 8) A factor of success in the implementation of an AI project is integrating intelligent systems into the workplace.

Answer: TRUE Diff: 2 Page Ref: 639 9) As intelligent systems are implemented in an organization, managers tend to have an increased reliance on experts and analysts as decisions are made.

Answer: FALSE Diff: 2 Page Ref: 659 1 .

10) A common organizational change as intelligent systems are implemented is the creation of an analytics or BI department.

Answer: TRUE Diff: 2 Page Ref: 655 11) In terms of jobs that are at risk due to intelligent systems, a healthcare social worker would be an example of a low-risk job.

Answer: TRUE Diff: 2 Page Ref: 750 12) In terms of jobs that are at risk due to intelligent systems, a telemarketer would be an example of a low-risk job.

Answer: FALSE Diff: 2 Page Ref: 662 13) Under the idea of friendly AI, AI machines would be designed so that they will benefit humans rather than harm them.

Answer: TRUE Diff: 2 Page Ref: 668 14) Because Elon Musk represents a dystopian view of AI, he refuses to invest in any AI research.

Answer: FALSE Diff: 3 Page Ref: 667 15) Insurance underwriters are among the low at-risk occupations to lose their jobs to AI. Answer: FALSE Diff: 2 Page Ref: 664 16) Despite the fear, uncertainty, and panic related to job losses, many reports that AI will create.

Answer: TRUE Diff: 2 Page Ref: 663 17) COMPAS is an AI powered tool designed to be used as a decision aid in several US courts.

Answer: TRUE Diff: 1 Page Ref: 651 18) According to cyber-citizenship, there exist five major commandments of computer ethics.

Answer: FALSE Diff: 2 Page Ref: 650 19) Amazon's Echo/Alexa, Google Assistant, iPhone Siri, and other similar devices listen to what is going on.

Answer: TRUE Diff: 2 Page Ref: 646

2 .

20) AI development activities are primarily restricted to the United States.

Answer: FALSE Diff: 2 Page Ref: 646 21) Recreational therapists are among the high at-risk occupations to lose their jobs to AI. Answer: FALSE Diff: 2 Page Ref: 664 22) There is a shortage of millions of skilled workers (e.g., about 50,000 truck drivers in the United States), so automation will reduce millions of unfilled positions.

Answer: TRUE Diff: 2 Page Ref: 664 23) Changes in jobs and business processes will impact training, innovation, wages, and the nature of work itself.

Answer: TRUE Diff: 2 Page Ref: 665 24) It is considered a success tip to put technology teams in charge of intelligent technologies.

Answer: FALSE Diff: 2 Page Ref: 665 25) One of the suggestions for policymakers is to properly handle new technology-enabled technologies.

Answer: TRUE Diff: 2 Page Ref: 666 26) In opening vignette, "can we access the data that exist within our organization to meet our project goals?" helps with all of the following issues, except?

A) Need for external data sources B) Formats of the data sources–unstructured and structured data sources and the effort needed to pull all the data together in one place in a uniform structure C) Need enhanced for security, privacy and data deidentification measures D) Calculated variables, lookups, and routines/functions to autopopulate some of the columns Answer: C Diff: 2 Page Ref: 635 27) In opening vignette, "Have we considered data governance issues for data quality?" question will determine all of the following, except?

A) Granularity of the data and related issues B) Type of predictive models to employ C) Incorrect data types D) Bad values Answer: B Diff: 2 Page Ref: 635

3 .

28) As stated in the opening vignette, the solutions to the data science problem can be combinations of the following, except?

A) Cubes for the data warehouse querying B) Deep learning models C) Statistical analysis, hypothesis testing D) Exploratory data analysis, data visualization Answer: A Diff: 2 Page Ref: 635 29) Which step of the intelligent systems implementation process needs to provide the business case for intelligent systems?

A) Need assessment B) Preparations C) System acquisition D) System development Answer: A Diff: 1 Page Ref: 637 30) Which step of the intelligent systems implementation process deals with the determination to in source or outsource technologies determined?

A) Need assessment B) Preparations C) System acquisition D) System development Answer: C Diff: 2 Page Ref: 637 31) Which step of the intelligent systems implementation process checks the performance of systems against initial plans and metrics?

A) Need assessment B) Preparations C) System development D) impact assessment Answer: D Diff: 2 Page Ref: 637 32) Which of the following is not an ethical issue related to the use of intelligent systems?

A) What is the impact on jobs?

B) Is the level of ROI appropriate for the company?

C) Can the system be fair and unbiased?

D) How can control of a complex intelligent system be maintained?

Answer: B Diff: 2 Page Ref: 649

4 .

33) Which of the following is not one of the 10 Commandments of computer ethics?

A) Thou shalt not use a computer to harm other people B) Thou shalt not use a computer to steal C) Thou shalt not use a computer to bear false witness D) Thou shalt not use a computer to participate in activities frowned upon by government Answer: D Diff: 3 Page Ref: 650 34) Which of the following is not a way to leverage intelligent systems and business?

A) Customize the customer experience B) Increase customer engagement C) Use intelligent technologies to detect problems D) Increase price to account for development expenses Answer: D Diff: 3 Page Ref: 640 35) Mr. Smith, an experienced MIS developer attempted to implement an AI system for his company. The planning and implementation failed due to the frequent use of large data sources which were outside of Mr. Smith's past experience. What system development implementation issue does this align with?

A) Development approach B) Learning from data C) No clear view of how insights are generated D) Experiential learning theory Answer: A Diff: 3 Page Ref: 639 36) Which of the following is not a potential impact of intelligent systems on managers jobs?

A) Less expertise required to make decisions B) Slower decision-making based on complexity C) Redistribution of power among managers D) Automation of routine decisions Answer: B Diff: 2 Page Ref: 659 37) An emerging area of research and practice is employing data science technologies for studying organizational dynamics, personnel behavior, and redesigning the organization to better achieve its goals. Indeed, such analytics applications are known as ________.

A) Resource Analytics B) Emotional Intelligence Systems C) Human Resource Analytics D) People Analytics Answer: D Diff: 2 Page Ref: 658

5 .

38) Which of the following professions would be at the lowest risk of job loss due to the implementation of advanced intelligent systems?

A) Telemarketer B) Audiologist C) Insurance underwriter D) Tax preparer Answer: B Diff: 2 Page Ref: 664 39) Which of the following is not predicted to be one of the changes in the nature of work that may be caused by intelligent systems?

A) Many activities currently done by humans may be automated B) Productivity through the use of intelligent systems will grow C) AI will create many new jobs paying high salaries D) Changes would be rapid, and all jobs directly affected within a decade Answer: D Diff: 2 Page Ref: 665 40) In terms of AI, utopianist is likely to believe that ________.

A) AI will support humans B) AI will enable innovations C) AI will partner with humans D) All of the other answers are true Answer: D Diff: 2 Page Ref: 667 41) Even under the utopian view of AI, what issues will humans face?

A) Uncertainty about what to do with free time B) Short-term impacts on jobs and work C) Changes in the nature of work and life D) All of the other answers are true Answer: D Diff: 2 Page Ref: 667 42) Which of the following is the strategic issue, which often known referred to as the need for a "fabric" and connectivity?

A) Organizational alignment B) Security C) Connection of the silos of data D) Privacy Answer: C Diff: 2 Page Ref: 642

6 .

43) IoT strategy cycle includes all of the following, except?

A) Select appropriate analytical products B) Specify business goals and objectives C) Device analytical strategy and plan D) Empower employees for exploration of novel solutions Answer: D Diff: 2 Page Ref: 643 44) Strategy cycle about IoT implementation as proposed by SAS includes all of the following steps, except?

A) Establish data access and privacy handling standards B) Express an analytic strategy C) Evaluate the needs for edge analytics D) Continues improvement closes the loop Answer: A Diff: 3 Page Ref: 643 45) Which of following is not among the AI applications to the legal profession?

A) Analyzing data concerning regulatory conflicts to detect pattern B) Predicting results (e.g., likelihood to win) C) Deciding on the outcome of the complex litigations D) Providing legal advice to consumers Answer: C Diff: 2 Page Ref: 645 46) Which of the following is not a true statement about the privacy and privacy related issues?

A) Privacy is the right to be left alone and be free from unreasonable personal intrusions B) Privacy means different things to different people C) Privacy issues are getting less attention as the data collection methods are automated D) Privacy has long been related to legal, ethical, and social issues Answer: C Diff: 3 Page Ref: 646 47) Which of the following is not one of the virtual personal assistants that listens to what is going on?

A) Baidu B) Siri C) Alexa D) Google assistant Answer: A Diff: 1 Page Ref: 646

7 .

48) Which of the following refers to the workplace analytics?

A) Automation of the work being done B) Auto guiding employees to the tasks to be performed C) Using predictive analytics to assess completion times D) Using sensor-embedded badges to track employees Answer: D Diff: 1 Page Ref: 647 49) Which of the following is not among the key principles of responsible AI? A) Privacy and security B) Inclusiveness C) Transparency D) Predictive accuracy Answer: D Diff: 1 Page Ref: 652 50) ________ is the name used for "humans must be involved in making the ethical decisions in proportion to the risk level."

A) Safety B) Human agency C) Lawfulness and compliance D) Accountability Answer: B Diff: 1 Page Ref: 653 51) In the ________ step of the intelligent systems implementation process, it is necessary to examine the organization readiness for analytics and AI. A) Preparation B) System development C) Deployment D) Impact assessment Answer: A Diff: 1 Page Ref: 637 52) ________ is the right to be left alone and the right to be free from unreasonable personal intrusions.

A) Security B) Accountability C) Privacy D) Freedom of speech Answer: C Diff: 1 Page Ref: 646

8 .

53) Which of the following is not among the intelligent system deployment topics related to deployment strategy?

A) How to decide whether to do it yourself or use partners, or to outsource B) How to justify investments in intelligent projects C) How to overcome employees' resistance (e.g., fear of job loss) D) How to arrange replacement of people with robot teams Answer: D Diff: 2 Page Ref: 638 54) Which of the following is not among the steps suggested by KPMG regarding digital labor?

A) Establishing priority areas for technological innovation B) Developing a strategy and a plan for the employees C) Identify providers and partners for plans' execution D) Establishing a strategy to reverse digital labor initiatives Answer: D Diff: 2 Page Ref: 639 55) With respect to IoT implementation, IT and operational personnel have to work as one team rather than separate functions is often referenced to as ________.

A) Interoperability challenges B) Organizational alignment C) Connection of the silos of data D) Connected customers Answer: B Diff: 2 Page Ref: 641 56) The first step in the strategy to turning industrial IoT into competitive advantage is ________.

A) Express an analytic strategy B) Evaluate the needs for edge analytics C) Specify the business goals D) Select appropriate analytics solutions Answer: C Diff: 2 Page Ref: 642 57) With respect to legal issues related to intelligence system, all of the following questions are valid and important, except?

A) Who is liable if an enterprise finds itself bankrupt as a result of using the advice of an AIbased application?

B) Will the person who executed the intelligent systems be liable for the adverse outcomes?

C) Will auditing/accounting firms share the liability for failing to apply adequate auditing tests?

D) Will the software developers of intelligent systems be jointly liable?

Answer: B Diff: 3 Page Ref: 644

9 .

58) Which of the following is an AI application to legal professionals?

A) Identifying client who is not telling the truth B) Helping legal system to detect deceptive testimonies C) Providing legal advice to consumers D) Select which law offices to use for the legal case Answer: D Diff: 2 Page Ref: 645 59) Who owns (or perhaps more properly, who should own) one's private data?

A) Government B) Self C) Application D) Network provider Answer: B Diff: 2 Page Ref: 648 60) Which of the following is not considered among the ethical issues concerning intelligent systems?

A) Monitoring and securing sensitive physical assets B) Ethics in business intelligence (BI) and AI systems design C) Software piracy D) Electronic surveillance Answer: A Diff: 2 Page Ref: 649 61) Which of the following is not among the terms used to describe this focus on making AI more ethical, fair, and relatively harmless to the public?

A) Ethical AI B) Responsible AI C) Robust AI D) Trustworthy AI Answer: C Diff: 3 Page Ref: 651 62) In the context of responsible AI, the model's ability to describe and justify its prediction or decision is called ________.

A) Responsibility B) Intelligence C) Interpretability D) Consistency Answer: C Diff: 2 Page Ref: 652

10 .

63) General ethical principles of AI include all of the following, except?

A) Accountability B) Reliability, robustness, and security C) Lawfulness and compliance D) Human agency Answer: B Diff: 2 Page Ref: 652 64) Which of the following is among the epistemic principles of AI? A) Accountability B) Human agency C) Lawfulness and compliance D) Reliability, robustness, and security Answer: D Diff: 3 Page Ref: 652 65) The data science technologies for studying organizational dynamics, personnel behavior, and redesigning the organization to better achieve its goals is nowadays called ________.

A) Descriptive analytics B) Operational analytics C) People analytics D) Organizational analytics Answer: C Diff: 2 Page Ref: 658 66) The issue regarding intelligent systems is strongly debated nowadays due to all of the following, except?

A) They may automate and replace a large variety of jobs B) Organizations are reluctant to believe in their financial advantages C) The capabilities of these systems are growing rapidly D) Their comparative advantage over manual labor is very large and growing rapidly Answer: B Diff: 2 Page Ref: 661 67) Which of the following is not among the jobs that have already been replaced (at least partially) by intelligent systems?

A) Sports reporters B) Financial analysts and advisors C) Online marketers D) Audiologists Answer: D Diff: 2 Page Ref: 662

11 .

68) Which of the following jobs are at high risk of being replaced by intelligent systems?

A) First-Line supervisors of firefighting and prevention workers B) Emergency management directors C) Tax preparers D) Audiologists Answer: C Diff: 2 Page Ref: 664 69) Which of the following people said, "The development of full artificial intelligence could spell the end of the human race"?

A) Elon Musk B) Bill Gates C) Stephen Hawking D) Steve Jobs Answer: C Diff: 2 Page Ref: 666 70) A(n) ________ is a business user in an organization who has not been fully trained in mathematics and statistics concepts but has learned to use the analytics tools to build models for their own business use.

A) Business intelligence analyst B) Citizen data scientist C) Data whisperer D) Ordinary analytics professional Answer: B Diff: 2 Page Ref: 669 71) Discuss the major steps in the implementation of intelligent systems.

Answer: • Step 1 Need assessment. Need assessment needs to provide the business case for the intelligent systems, including their major parts.

• Step 2 Preparations. In this step, it is necessary to examine the organization readiness for analytics and AI. It is necessary to check available resources, employees' attitudes for the change, projects' priorities, and so on.

• Step 3 System acquisition. Organizations need to decide on in-house or outsourcing approach (make or buy) or on a combination of the two and possibly with partnership with a vendor or another company. A consultant may help at this step.

• Step 4 System development. Regardless of who will develop the system, certain activities need to be done. These include security, integration with other systems, project management preparation, and other activities.

• Step 5 Impact assessment. It is necessary to check the performance of the systems against plans.

Diff: 2 Page Ref: 637-638

12 .

72) What are the three primary impacts of intelligent systems?

Answer: The three primary areas of impact of intelligent systems include: 1. Impacts on the organization (including structure, employees, management, industries and the competitive environment), 2. Work and jobs (including automation, safes and unsafe jobs, and the nature of work), and 3. Unintended impacts (including the dangers of AI and the dangers of analytics).

Diff: 2 Page Ref: 637 73) The growth of intelligent systems can create a number of potential legal issues. Provide an example of a legal issue and discuss how intelligent systems could have an impact and ways that this impact could be better understood and/or mitigated.

Answer: Student selection and analysis of issues will vary, but issues may be selected from the group represented on pages 644 through 646.

Diff: 2 Page Ref: 644-646 74) Intelligent technologies have the potential to collect a wide amount of information on individuals both at work and in their private time. Do you feel this is appropriate, and how should it be regulated?

Answer: Student opinions and perceptions will vary along with their potential solutions to these issues.

Diff: 2 Page Ref: 646 75) Select two ethical issues related to the growth or use of intelligent systems. Briefly define these issues and discuss and exemplify the ethical concerns.

Answer: Student selection of issues and analysis of that issue will vary greatly, but students may select one of the issues listed on page 649.

Diff: 3 Page Ref: 649 76) List the 10 Commandments of computer ethics. Select one and discuss why you agree or disagree with it.

Answer: The "Ten Commandments" of computer ethics are: 1. Thou shalt not use a computer to harm other people.

2. Thou shalt not interfere with other people's computer work.

3. Thou shalt not snoop around in other people's files.

4. Thou shalt not use a computer to steal.

5. Thou shalt not use a computer to bear false witness.

6. Thou shalt not use or copy software for which you have not paid.

7. Thou shalt not use other people's computer resources without authorization.

8. Thou shalt not appropriate other people's intellectual output.

9. Thou shalt not think about the social consequences of the program you write.

10. Thou shalt not use a computer in ways that show consideration and respect.

Student selection and comment on a particular commandment will vary.

Diff: 3 Page Ref: 650

13 .

77) List and discuss the four-step approach identified by KPMG for successful implementation of digital labor.

Answer: The steps identified by KPMG (2018) include: • Establishing priority areas for technological innovation.

• Developing a strategy and a plan for the employees.

• Identify providers and partners for plans' execution.

• Establishing a strategy and plans to realize benefits from the digital labor initiatives.

Diff: 2 Page Ref: 639 78) List and discuss the three main issues in AI and business analytics implementation (as identified by Shchutskaya, 2017).

Answer: 1. Development approach. Business analytic and AI systems requires an approach different from that of other IT/computer systems. Specifically, it is necessary to identify and deal with different and frequently large data sources. It is necessary to cleanse and curate these data. Also, if learning is involved, one needs to use machine training. Thus, special methodologies are needed.

2. Learning from data. Many AI and business analytics involve learning. The quality of the input data determines the quality of the applications. Also, the learning mechanism is important.

Therefore, data accuracy is critical. In learning, systems must be able to deal with changing environmental conditions. Data should be organized in databases, not in files.

3. No clear view is available of how insights are generated. AI, IoT, and business analytic systems generate insights, conclusions, and recommendations based on the analysis of the data collected. Given that data are frequently collected by sensors and there are different types of them, we may not have a clear view of the insights that are generated.

Diff: 2 Page Ref: 639 79) Discuss how the growth in analytics can affect organizational structure.

Answer: An often-cited change in organizational structure as additional analytics and BI projects are undertaken is the growth or creation of a separate analytics, BI or data science department. This may be a holy new department or be created from or into existing MIS departments. With this growth comes the creation of new positions related to these specialties and possibly the elimination of old positions that have become less relevant to overall company strategy. Conversely, in other organizations this results in embedding analytics specialties within each functional area (such as marketing, operations or finance).

Diff: 3 Page Ref: 655-656 80) Discuss how the implementation of intelligent systems will change the nature of your current job. How can you become immune to losing your job to AI/machines?

Answer: Student responses will vary based on the type of job that they identify, but many discussions will focus on how some positions will be eliminated and others created and/or augmented. Students may also discuss how new or changed positions will interact with intelligent systems.

Diff: 2 Page Ref: 664-667

14 .