Business Intelligence and Analytics
Exam Materials
Course Introduction
Business Intelligence and Analytics focuses on the processes, methodologies, and technologies used to transform raw data into meaningful and actionable information for strategic decision making in organizations. This course covers key concepts such as data warehousing, data mining, business analytics, and visualization techniques. Students will learn how to leverage various tools to gather, analyze, and present data, enabling organizations to gain competitive advantages, improve operational efficiency, and make informed business decisions. Practical case studies and hands-on projects are integrated to build essential analytical skills relevant to real-world business environments.
Recommended Textbook
Data Mining A Tutorial Based Primer 1st Edition by Richard Roiger
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14 Chapters
167 Verified Questions
167 Flashcards
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Chapter 1: Data Mining: a First View
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22 Verified Questions
22 Flashcards
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Sample Questions
Q1) If a customer is spending more than expected, the customer's intrinsic value is ________ their actual value.
A) greater than
B) less than
C) less than or equal to
D) equal to Matching Questions
Answer: B
Q2) Develop a profile for credit card customers likely to carry an average monthly balance of more than $1000.00.
A)supervised learning
B)unsupervised clustering
C)data query
Answer: A
Q3) Which of the following is not a characteristic of a data warehouse?
A) contains historical data
B) designed for decision support
C) stores data in normalized tables
D) promotes data redundancy
Answer: C
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Page 3
Chapter 2: Data Mining: a Closer Look
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16 Verified Questions
16 Flashcards
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Sample Questions
Q1) Which of the following is a common use of unsupervised clustering?
A) detect outliers
B) determine a best set of input attributes for supervised learning
C) evaluate the likely performance of a supervised learner model
D) determine if meaningful relationships can be found in a dataset
E) All of a,b,c, and d are common uses of unsupervised clustering.
Answer: E
Q2) Which statement is true about neural network and linear regression models?
A) Both models require input attributes to be numeric.
B) Both models require numeric attributes to range between 0 and 1.
C) The output of both models is a categorical attribute value.
D) Both techniques build models whose output is determined by a linear sum of weighted input attribute values.
E) More than one of a,b,c or d is true.
Answer: A
Q3) How many instances were classified as an accept by Model X?
Answer: 35
Q4) What percent of the instances were correctly classified?
Answer: 60%
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Chapter 3: Basic Data Mining Techniques
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13 Verified Questions
13 Flashcards
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Sample Questions
Q1) An evolutionary approach to data mining.
A) backpropagation learning
B) genetic learning
C) decision tree learning
D) linear regression
Answer: B
Q2) Based on the two-item set table, which of the following is not a possible two-item set rule?
A) IF Life Ins Promo = Yes THEN Magazine Promo = Yes
B) IF Watch Promo = No THEN Magazine Promo = Yes
C) IF Card Insurance = No THEN Magazine Promo = Yes
D) IF Life Ins Promo = No THEN Card Insurance = No
Answer: D
Q3) A data mining algorithm is unstable if
A) test set accuracy depends on the ordering of test set instances.
B) the algorithm builds models unable to classify outliers.
C) the algorithm is highly sensitive to small changes in the training data.
D) test set accuracy depends on the choice of input attributes.
Answer: C
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Chapter 4: An Excel-Based Data Mining Tool
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12 Verified Questions
12 Flashcards
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Sample Questions
Q1) A particular categorical attribute value has a predictiveness score of 0.3 and a predictability score of 0.3. The attribute value is
A) necessary but not sufficient for class membership.
B) sufficient but not necessary for class membership.
C) necessary and sufficient for class membership.
D) neither necessary nor sufficient for class membership.
Q2) A particular categorical attribute value has a predictiveness score of 0.5 and a predictability score of 1.0. The attribute value is
A) necessary but not sufficient for class membership.
B) sufficient but not necessary for class membership.
C) necessary and sufficient for class membership.
D) neither necessary nor sufficient for class membership.
Q3) What is the predictability score for the attribute value medium risk?
A) 0.10
B) 0.20
C) 0.25
D) 0.50
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6
Chapter 5: Knowledge Discovery in Databases
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10 Flashcards
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Sample Questions
Q1) KDD has been described as the application of ___ to data mining.
A) the waterfall model
B) object-oriented programming
C) the scientific method
D) procedural intuition
Q2) This step of the KDD process model deals with noisy data.
A) Creating a target dataset
B) data preprocessing
C) data transformation
D) data mining
Q3) Attibutes may be eliminated from the target dataset during this step of the KDD process.
A) creating a target dataset
B) data preprocessing
C) data transformation
D) data mining
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Chapter 6: The Data Warehouse
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Sample Questions
Q1) This process removes redundancies that may be present in a data model.
A) abstraction
B) granularization
C) standardization
D) normalization
Q2) Which of the following is not an example of a slice operation?
A) Select all cells where purchase category = retail.
B) Select all cells where purchase category = retail or vehicle.
C) Provide a spreadsheet of quarter and region information for all cells pertaining to restaurant.
D) Identify the region of peak travel expenditure for each quarter.
Q3) Operational databases are designed to support _____ whereas decision support systems are design to support __________.
A) transactional processing, data analysis
B) data analysis, transactional processing
C) independent data marts, dependent data marts
D) dependent data marts, independent data marts
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8
Chapter 7: Formal Evaluation Techniques
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13 Flashcards
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Sample Questions
Q1) The correlation between the number of years an employee has worked for a company and the salary of the employee is 0.75. What can be said about employee salary and years worked?
A) There is no relationship between salary and years worked.
B) Individuals that have worked for the company the longest have higher salaries.
C) Individuals that have worked for the company the longest have lower salaries.
D) The majority of employees have been with the company a long time.
E) The majority of employees have been with the company a short period of time.
Q2) We have performed a supervised classification on a dataset containing 100 test set instances. Eighty of the test set instances were correctly classified. The 95% test set accuracy confidence boundaries are:
A) 76% and 84%
B) 72% and 88%
C) 78% and 82%
D) 70% and 90%
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9
Chapter 8: Neural Networks
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10 Flashcards
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Sample Questions
Q1) Epochs represent the total number of
A) input layer nodes.
B) passes of the training data through the network.
C) network nodes.
D) passes of the test data through the network.
Q2) A feed-forward neural network is said to be fully connected when
A) all nodes are connected to each other.
B) all nodes at the same layer are connected to each other.
C) all nodes at one layer are connected to the nodes in the next higher layer.
D) all hidden layer nodes are connected to all output layer nodes.
Q3) This neural network explanation technique is used to determine the relative importance of individual input attributes.
A) sensitivity analysis
B) average member technique
C) mean squared error analysis
D) absolute average technique
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10
Chapter 9: Building Neural Networks With Ida
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4 Flashcards
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Sample Questions
Q1) The test set accuracy of a backpropagation neural network can often be improved by
A) increasing the number of epochs used to train the network.
B) decreasing the number of hidden layer nodes.
C) increasing the learning rate.
D) decreasing the number of hidden layers.
Q2) This type of supervised network architecture does not contain a hidden layer.
A) backpropagation
B) perceptron
C) self-organizing map
D) genetic
Q3) Two classes each of which is represented by the same pair of numeric attributes are linearly separable if
A) at least one of the pairs of attributes shows a curvilinear relationship between the classes.
B) at least one of the pairs of attributes shows a high positive correlation between the classes.
C) at least one of the pairs of attributes shows a high positive correlation between the classes
D) a straight line partitions the instances of the two classes
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Chapter 10: Statistical Techniques
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13 Flashcards
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Sample Questions
Q1) Logistic regression is a ________ regression technique that is used to model data having a _____outcome.
A) linear, numeric
B) linear, binary
C) nonlinear, numeric
D) nonlinear, binary
Q2) Simple regression assumes a __________ relationship between the input attribute and output attribute.
A) linear
B) quadratic
C) reciprocal
D) inverse
Q3) The probability of a hypothesis before the presentation of evidence.
A) a priori
B) subjective
C) posterior
D) conditional
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Page 12
Chapter 11: Specialized Techniques
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10 Verified Questions
10 Flashcards
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Sample Questions
Q1) Which of the following is a fundamental difference between bagging and boosting?
A) Bagging is used for supervised learning. Boosting is used with unsupervised clustering.
B) Bagging gives varying weights to training instances. Boosting gives equal weight to all training instances.
C) Bagging does not take the performance of previously built models into account when building a new model. With boosting each new model is built based upon the results of previous models.
D) With boosting, each model has an equal weight in the classification of new instances. With bagging, individual models are given varying weights.
Q2) Usage profiles for Web-based personalization contain several
A) pageviews
B) clickstreams
C) cookies
D) session files
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13
Chapter 12: Rule-Based Systems
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15 Flashcards
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Sample Questions
Q1) The main components of an expert system include a Knowledge base, a user interface and
A) a database
B) an explanation facility
C) an inference engine
D) a constraint propagator
Q2) Knowledge acquisition involves one or more experts and a
A) systems analyst.
B) knowledge engineer.
C) knowledge designer.
D) systems engineer.
Q3) An expert system with an empty knowledge base.
A) expert system shell
B) inference shell
C) state space
D) goal tree
Q4) Construct a goal tree using the following production rules. Assume the goal is g.
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Chapter 13: Managing Uncertainty in Rule-Based Systems
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10 Flashcards
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Sample Questions
Q1) A car mechanic tells you that there is a 75% chance that your car will need major repair work within the next six months. This statement is an example of
A) an objective probability.
B) an experimental probability.
C) a subjective probability.
D) a fuzzy probability.
Q2) With Bayes theorem the probability of hypothesis H- specified by PH) - is referred to as
A) an a priori probability
B) a conditional probability
C) a posterior probability
D) a bidirectional probability
Q3) Probabilities based on frequency distributions obtained through sampling.
A) objective
B) experimental
C) subjective
D) inexact
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Chapter 14: Intelligent Agents
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6 Flashcards
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Sample Questions
Q1) Data mining techniques and intelligent agents are similar in that they
A) are both used for hypotheses testing.
B) both have the ability to learn.
C) are goal directed.
D) both build models from data.
Q2) An agent's ability to choose its actions in the context of other agents.
A) autonomy
B) cooperation
C) adaptivity
D) coordination
Q3) Autonomy is an agent's ability to
A) react to a changing environment.
B) act without direct intervention from others.
C) confer with other agents.
D) react to sensory information received from the environment.
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