WGU D491
INTRODUCTION TO ANALYTICS
60-QUESTION PRACTICE EXAM
2026 STUDY EDITION
60 4 60
ORIGINAL QUESTIONS ANSWER CHOICES RATIONALES
Original, scenario-based questions for focused review of analytics concepts, roles, lifecycle,
value, and evidence-based decisions.
Designed around the published course scope for Introduction to Analytics, including data analytics roles and
functions, the analytics lifecycle, statistics, problem solving, and programming concepts.
Includes a separate answer key and a source framework for further review.
,WGU D491 | INTRODUCTION TO ANALYTICS PRACTICE EXAM
HOW TO USE THIS PRACTICE EXAM
Choose the single best answer for each question before reading the rationale. For exam simulation, complete all
questions first, then use the answer key to score your work. For learning, read each rationale and explain why
the alternatives are less suitable.
CORE DOMAINS
• Analytics roles, business intelligence, and data science fundamentals
• Data analytics lifecycle and problem framing
• Stakeholders, hypotheses, project scope, and business value
• Data sources, quality, cleaning, transformation, and documentation
• Descriptive statistics, distributions, sampling, and uncertainty
• Diagnostic, predictive, and prescriptive analysis
• Visualization, reporting, and stakeholder communication
• Privacy, fairness, ethics, data governance, and responsible use
INTRODUCTION
This practice assessment reviews foundational concepts in data analytics and how organizations use data to
answer meaningful questions. It covers roles within analytics teams, the lifecycle from discovery through
deployment, data quality, introductory statistics, analytical approaches, visualization, and responsible data use.
Questions include practical scenarios requiring the learner to connect evidence with business decisions and
recognize limitations, risks, and stakeholder needs. Each item contains four answer choices, one best answer,
and a rationale explaining the core concept. The set is intended to support focused review and self-assessment,
while the learner’s current course materials and degree plan remain the final guide to course-specific emphasis.
QUESTION SET
The answer positions are balanced across A, B, C, and D. Questions are written for practice and are not
represented as live or proprietary assessment items.
2026 STUDY EDITION Page 2
, WGU D491 | INTRODUCTION TO ANALYTICS PRACTICE EXAM
ANALYTICS ROLES AND FOUNDATIONS
Question 1. A retail manager wants to understand why customers abandon online carts.
Which description best captures data analytics?
A. Creating charts before defining the problem
B. Using data and appropriate analysis to develop evidence that supports a decision
C. Replacing all business judgment with an algorithm
D. Collecting as much data as possible regardless of the decision
Correct answer: B. Using data and appropriate analysis to develop evidence that supports a
decision
RATIONALE: Analytics connects data to a defined question and a decision. Collecting data or making charts
without a purpose can create activity without useful insight; analytics should support judgment rather than
replace context.
Question 2. A project sponsor explains the business problem, secures resources, and
approves the intended outcomes. Which role is the sponsor performing?
A. Providing business direction, ownership, and resources for the initiative
B. Designing every database table
C. Writing all data-cleaning scripts
D. Maintaining the production data pipeline
Correct answer: A. Providing business direction, ownership, and resources for the initiative
RATIONALE: A sponsor provides organizational backing, helps define the desired business outcome, and secures
resources. Technical specialists may build pipelines or transform data, but those tasks are not the sponsor’s
primary function.
Question 3. Which responsibility most directly belongs to a data engineer on an
analytics team?
A. Approving the business strategy without stakeholder input
B. Owning all organizational change communication
C. Choosing the final marketing message based only on personal preference
D. Building and maintaining reliable systems that ingest, transform, and deliver data
Correct answer: D. Building and maintaining reliable systems that ingest, transform, and deliver
data
RATIONALE: Data engineers make data usable and reliably available through pipelines, storage, and
transformation systems. Business strategy, change communication, and marketing decisions require other roles
and stakeholder responsibilities.
Question 4. A data scientist is asked to estimate which customers are most likely to
cancel a subscription. Which task most closely matches that role?
A. Approving employee vacation requests
B. Developing and evaluating analytical or predictive models
C. Physically installing network cabling
D. Entering every transaction manually
Correct answer: B. Developing and evaluating analytical or predictive models
RATIONALE: Data scientists commonly use statistical and computational methods to build and assess models.
Model results should be evaluated against the business objective and validated data; unrelated operational
tasks do not describe this analytical responsibility.
2026 STUDY EDITION Page 3
INTRODUCTION TO ANALYTICS
60-QUESTION PRACTICE EXAM
2026 STUDY EDITION
60 4 60
ORIGINAL QUESTIONS ANSWER CHOICES RATIONALES
Original, scenario-based questions for focused review of analytics concepts, roles, lifecycle,
value, and evidence-based decisions.
Designed around the published course scope for Introduction to Analytics, including data analytics roles and
functions, the analytics lifecycle, statistics, problem solving, and programming concepts.
Includes a separate answer key and a source framework for further review.
,WGU D491 | INTRODUCTION TO ANALYTICS PRACTICE EXAM
HOW TO USE THIS PRACTICE EXAM
Choose the single best answer for each question before reading the rationale. For exam simulation, complete all
questions first, then use the answer key to score your work. For learning, read each rationale and explain why
the alternatives are less suitable.
CORE DOMAINS
• Analytics roles, business intelligence, and data science fundamentals
• Data analytics lifecycle and problem framing
• Stakeholders, hypotheses, project scope, and business value
• Data sources, quality, cleaning, transformation, and documentation
• Descriptive statistics, distributions, sampling, and uncertainty
• Diagnostic, predictive, and prescriptive analysis
• Visualization, reporting, and stakeholder communication
• Privacy, fairness, ethics, data governance, and responsible use
INTRODUCTION
This practice assessment reviews foundational concepts in data analytics and how organizations use data to
answer meaningful questions. It covers roles within analytics teams, the lifecycle from discovery through
deployment, data quality, introductory statistics, analytical approaches, visualization, and responsible data use.
Questions include practical scenarios requiring the learner to connect evidence with business decisions and
recognize limitations, risks, and stakeholder needs. Each item contains four answer choices, one best answer,
and a rationale explaining the core concept. The set is intended to support focused review and self-assessment,
while the learner’s current course materials and degree plan remain the final guide to course-specific emphasis.
QUESTION SET
The answer positions are balanced across A, B, C, and D. Questions are written for practice and are not
represented as live or proprietary assessment items.
2026 STUDY EDITION Page 2
, WGU D491 | INTRODUCTION TO ANALYTICS PRACTICE EXAM
ANALYTICS ROLES AND FOUNDATIONS
Question 1. A retail manager wants to understand why customers abandon online carts.
Which description best captures data analytics?
A. Creating charts before defining the problem
B. Using data and appropriate analysis to develop evidence that supports a decision
C. Replacing all business judgment with an algorithm
D. Collecting as much data as possible regardless of the decision
Correct answer: B. Using data and appropriate analysis to develop evidence that supports a
decision
RATIONALE: Analytics connects data to a defined question and a decision. Collecting data or making charts
without a purpose can create activity without useful insight; analytics should support judgment rather than
replace context.
Question 2. A project sponsor explains the business problem, secures resources, and
approves the intended outcomes. Which role is the sponsor performing?
A. Providing business direction, ownership, and resources for the initiative
B. Designing every database table
C. Writing all data-cleaning scripts
D. Maintaining the production data pipeline
Correct answer: A. Providing business direction, ownership, and resources for the initiative
RATIONALE: A sponsor provides organizational backing, helps define the desired business outcome, and secures
resources. Technical specialists may build pipelines or transform data, but those tasks are not the sponsor’s
primary function.
Question 3. Which responsibility most directly belongs to a data engineer on an
analytics team?
A. Approving the business strategy without stakeholder input
B. Owning all organizational change communication
C. Choosing the final marketing message based only on personal preference
D. Building and maintaining reliable systems that ingest, transform, and deliver data
Correct answer: D. Building and maintaining reliable systems that ingest, transform, and deliver
data
RATIONALE: Data engineers make data usable and reliably available through pipelines, storage, and
transformation systems. Business strategy, change communication, and marketing decisions require other roles
and stakeholder responsibilities.
Question 4. A data scientist is asked to estimate which customers are most likely to
cancel a subscription. Which task most closely matches that role?
A. Approving employee vacation requests
B. Developing and evaluating analytical or predictive models
C. Physically installing network cabling
D. Entering every transaction manually
Correct answer: B. Developing and evaluating analytical or predictive models
RATIONALE: Data scientists commonly use statistical and computational methods to build and assess models.
Model results should be evaluated against the business objective and validated data; unrelated operational
tasks do not describe this analytical responsibility.
2026 STUDY EDITION Page 3
