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Question #:1
MapReduce is designed to process data in which way?
A few large files split into blocks processed in parallel across multiple machines
Many small files processed serially on one machine
A few large files split into blocks processed serially on one machine
Many small files processed in parallel across multiple machines
Answer: A
Explanation
MapReduce is designed to process a few large files that are split into blocks and then processed in parallel across multiple machines. This approach allows for efficient distributed processing of large datasets.
Question #:2
What is a key consideration when preparing a presentation intended for analysts?
Describe how to implement the model
Provide talking points to promote or evangelize the project
Emphasize the business benefits of implementing the model
Focus on clean simple-to-understand visuals
Answer: D
Explanation
Analysts value clarity and interpretability in data presentations. Clean, simple-to-understand visuals help them accurately assess the data, model outputs, and insights without unnecessary complexity.
Question #:3
Refer to the exhibit.
To predict whether or not a customer will renew their annual property insurance policy, an insurance company built and operationalized a naïve Bayes classification model. In the model, there are two class labels, renewal and non-renewal, that are assigned to each customer based on their attributes.
A subset of the key attributes, their values, and corresponding conditional probabilities are provided in the exhibit.
A customer has the following attributes:
# Age is greater than 65 years
# Owns their own home
# Renewal month is August
If 20% of customers do not renew the police every year, what is the in the naïve score for a renewal Bayesian model for the customer described above? 0.0022
Answer: D
Explanation
The formula for is: Naïve Bayes
A.
B. C.
D.
For the renewal class, we are given:
# P(Class = Renewal) = 0.8 (since 80% renew the policy)
# P(Age > 65 years | Renewal) = 0.3
# P(Housing = Own | Renewal) = 0.9
# P(Renewal Month = August | Renewal) = 0.1
P(Renewal) = P(Renewal) × P(Age > 65 years | Renewal) × P(Housing = Own | Renewal) × P (Renewal Month = August | Renewal)
P(Renewal) = 0.8 × 0.3 × 0.9 × 0.1 = 0.0216
Question #:4
Which visualization technique should be avoided?
Using a small number of contrasting colors to draw distinctions
Using tables of numbers to present all of the data visually
Achieving a high data-ink ratio
Using visuals to illustrate key points
Answer: B
Explanation
Using tables of numbers to present all of the data visually should be avoided, as it can overwhelm the audience and make it harder to interpret key insights. Instead, visualizations should simplify data and focus on illustrating trends or patterns effectively.
Question #:5
What is the similarity between the matrix and array data structures in R?
Both structures can contain only integers
Both structures can only contain one data type
Both structures can store multiple data types
A. B. C. D.
Both structures must be 2-dimensional
Answer: B
Explanation
Both matrix and array data structures in R can only contain one data type across all their elements, ensuring consistency in the structure.
Question #:6
In hypothesis testing, when does a Type I error occur?
Null hypothesis is rejected when it is actually false
Null hypothesis is rejected when it is actually true
Null hypothesis is accepted when it is actually false
Null hypothesis is accepted when it is actually true
Answer: B
Explanation
A Type I error occurs when the null hypothesis is rejected even though it is actually true. This is also known as a "false positive" in hypothesis testing.
Question #:7
In time series analysis, what statement describes a MA(q) process?
Current deviation from the time series mean depends on the q previous deviations
Current deviation from the time series mean depends on the quotient q
Current time series value depends on the q previous values
Current time series value depends on the fitted polynomial of order q
Answer: A
Explanation
In a Moving Average (MA) process of order , the current deviation from the mean is modeled as a linear q combination of the previous deviations (errors). q
Question #:8
A.
Which Hadoop service responds to requests for compute and memory resources?
Application Manager
DataNode
Scheduler
Application Master
Answer: C
Explanation
The Scheduler in Hadoop is responsible for allocating compute and memory resources across various applications running on the cluster. It decides how resources are distributed based on policies and availability.
Question #:9
You have been given a task to improve sales force compensation of your organization. As a result of a study, your team decides to classify personnel as follows:
# Did not meet quota
# Met quota
# Exceeded 150% of quota
In which data analytics lifecycle phase should you define these categories for analysis purposes?
Model building
Communicate results
Operationalize
Model planning
Answer: D
Explanation
Defining categories such as performance levels falls under the model planning phase, where data is prepared and structured for analysis. This step involves selecting techniques and identifying how data will be used in modeling.
Question #:10
What are categorized as cluster and workflow management tools for Hadoop?
Flume, Sqoop, and Storm
B. C.
D.
Drill, Hive, and HBase
Spark, Tez, and Cassandra
Ambari, Oozie, and Zookeeper
Answer: D
Explanation
Ambari, Oozie, and Zookeeper are tools used for cluster and workflow management in Hadoop. Ambari manages and monitors clusters, Oozie handles workflow scheduling, and Zookeeper coordinates distributed processes.
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