WGU D491 INTRODUCTION TO ANALYTICS EXAM
AND PRACTICE EXAM TEST BANK WITH STUDY
GUIDE 2026 | ACCURATE ACTUAL EXAM
FREQUENTLY TESTED QUESTIONS WITH
DETAILED ANSWERS | GUARANTEED PASS |
GRADED A | LATEST UPDATE
A data analyst works at an e-commerce company that
wants to understand its customer churn rate. Their
manager has tasked them with conducting a data analytics
project to identify customers at risk of churn and offer
these customers targeted promotions to retain their
business. What is the primary purpose of the data
analytics project's results in this scenario?
To optimize inventory management
To identify customer preferences
To compare the company's churn rate to industry
benchmarks
To predict customer churn risk
To predict customer churn risk
A data analyst works at an e-commerce company that
wants to understand its customer churn rate. Their
manager has tasked them with conducting a data analytics
,project to identify customers at risk of churn and offer
these customers targeted promotions to retain their
business. What is the most suitable form of deliverable in
this scenario?
Supply chain improvements
Lists of at-risk customers
Monthly sales reports
Updated website design
Lists of at-risk customers
A retail company wants to improve its sales and customer
satisfaction by analyzing customer data. The company
hired a data analytics team, which has access to the
company's customer database, including transaction
records, demographic information, and customer feedback.
The data analytics team will work closely with the
marketing and IT departments to create actionable
insights for the company. The team has three months to
complete the project, and the company's budget allows
purchasing additional software tools or training, if
necessary. What is the most critical resource for the data
analytics project?
The company's inventory records
The company's financial statements
,The customer database
The employee records
The customer database
What is the advantage of using a decision tree over a linear
regression model in a data analytics project?
Decision trees are faster and require fewer computational
resources.
Decision trees can produce more accurate predictions.
Decision trees can handle missing data more effectively.
Decision trees can handle nonlinear relationships between
variables.
Decision trees can handle nonlinear relationships between
variables.
Decision trees can model complex, nonlinear relationships
between variables, while linear regression models are
limited to linear relationships.
A retail grocer wants to use association rules in retail
marketing to increase sales.
What would be the impact of using an association rule on
sales data?
, - By analyzing sales data, the data analyst can apply
association rules to discover frequent item sets, which are
groups of items often purchased together.
- By analyzing sales data, the data analyst can apply
association rules to discover stockpiling behavior, which
can be used for coupons.
- By analyzing sales data, the data analyst can apply
association rules to predict revenues in the future, which
can be used in business strategy.
- By analyzing sales data, the data analyst can apply
association rules to discover rare purchases, which can be
used for future product generation.
By analyzing sales data, the data analyst can apply
association rules to discover frequent item sets, which are
groups of items often purchased together.
A company wants to predict the likelihood of a customer
responding to a marketing campaign. The data set
contains both numerical and categorical variables.
Which analytics technique should the company use?
Logistic regression
K-means clustering
Random forest
Principal component analysis (PCA)
AND PRACTICE EXAM TEST BANK WITH STUDY
GUIDE 2026 | ACCURATE ACTUAL EXAM
FREQUENTLY TESTED QUESTIONS WITH
DETAILED ANSWERS | GUARANTEED PASS |
GRADED A | LATEST UPDATE
A data analyst works at an e-commerce company that
wants to understand its customer churn rate. Their
manager has tasked them with conducting a data analytics
project to identify customers at risk of churn and offer
these customers targeted promotions to retain their
business. What is the primary purpose of the data
analytics project's results in this scenario?
To optimize inventory management
To identify customer preferences
To compare the company's churn rate to industry
benchmarks
To predict customer churn risk
To predict customer churn risk
A data analyst works at an e-commerce company that
wants to understand its customer churn rate. Their
manager has tasked them with conducting a data analytics
,project to identify customers at risk of churn and offer
these customers targeted promotions to retain their
business. What is the most suitable form of deliverable in
this scenario?
Supply chain improvements
Lists of at-risk customers
Monthly sales reports
Updated website design
Lists of at-risk customers
A retail company wants to improve its sales and customer
satisfaction by analyzing customer data. The company
hired a data analytics team, which has access to the
company's customer database, including transaction
records, demographic information, and customer feedback.
The data analytics team will work closely with the
marketing and IT departments to create actionable
insights for the company. The team has three months to
complete the project, and the company's budget allows
purchasing additional software tools or training, if
necessary. What is the most critical resource for the data
analytics project?
The company's inventory records
The company's financial statements
,The customer database
The employee records
The customer database
What is the advantage of using a decision tree over a linear
regression model in a data analytics project?
Decision trees are faster and require fewer computational
resources.
Decision trees can produce more accurate predictions.
Decision trees can handle missing data more effectively.
Decision trees can handle nonlinear relationships between
variables.
Decision trees can handle nonlinear relationships between
variables.
Decision trees can model complex, nonlinear relationships
between variables, while linear regression models are
limited to linear relationships.
A retail grocer wants to use association rules in retail
marketing to increase sales.
What would be the impact of using an association rule on
sales data?
, - By analyzing sales data, the data analyst can apply
association rules to discover frequent item sets, which are
groups of items often purchased together.
- By analyzing sales data, the data analyst can apply
association rules to discover stockpiling behavior, which
can be used for coupons.
- By analyzing sales data, the data analyst can apply
association rules to predict revenues in the future, which
can be used in business strategy.
- By analyzing sales data, the data analyst can apply
association rules to discover rare purchases, which can be
used for future product generation.
By analyzing sales data, the data analyst can apply
association rules to discover frequent item sets, which are
groups of items often purchased together.
A company wants to predict the likelihood of a customer
responding to a marketing campaign. The data set
contains both numerical and categorical variables.
Which analytics technique should the company use?
Logistic regression
K-means clustering
Random forest
Principal component analysis (PCA)
