WGU D491 INTRODUCTION TO ANALYTICS EXAM
(2025/2026) RANDOMIZED QUESTION ORDER VERIFIED
A+ EXAM SOLUTIONS UNIQUE STUDY FORMAT
A healthcare company wants to predict which patients are at risk of developing a
certain medical condition. Which model is commonly used for this type of
analysis?
• Decision tree
• Association rules
• K-means clustering
• Logistic regression
Logistic regression
Logistic regression is a model that predicts the probability of an event occurring.
During a data analytics project, which phase focuses on developing training and
test datasets, refining models, and assessing the validity and predictive power of
the models?
• Model execution
• Data preparation
• Model planning
• Operationalize
Model execution
What is the main purpose of the model execution phase in a data analytics
project?
• To clean, transform, and aggregate data for analysis
,• To develop datasets, refine models, and assess validity
• To select appropriate models based on project goals
• To deploy the model and calculate its financial impact
To develop datasets, refine models, and assess validity
Which activities should the data analytics team perform during the model
execution phase of this project?
• Creating data visualizations and capturing essential predictors
• Deploying the model and measuring its return on investment
• Generating training and test sets and refining models to enhance
performance
• Grouping categorical variables and standardizing numeric values
Generating training and test sets and refining models to enhance performance
Which tool is suitable for a data analytics team to use during the model execution
phase of a project?
• SAS Enterprise
• Miner Tableau
• KNIME
• Microsoft Excel
SAS Enterprise Miner
Which phase of a data analytics project involves articulating findings and
outcomes for stakeholders while considering caveats, assumptions, and
limitations?
• Data preparation
• Communicate results
• Operationalize
,• Model development
Communicate results
What is the purpose of the communicate results phase in a data analytics project?
• Presenting findings and outcomes to stakeholders
• Preparing and managing data for analysis
• Evaluating the project's financial and technical results
• Creating and refining analytical models
Presenting findings and outcomes to stakeholders
Which activity should the data analytics team focus on during the communicate
results phase
• Presenting key findings to stakeholders and evaluating the project's success
• Building and testing different predictive models for customer churn
• Analyzing the financial impact of the project on the company's revenue and
customer retention
• Performing data cleaning and transforming raw data into usable formats
Presenting key findings to stakeholders and evaluating the project's success
Which tools are commonly used for communicating results in data analytics
projects?
• Predictive modeling software and programming languages
• Data visualization tools and presentation software
• Database management systems and data warehouses
• Text editors and spreadsheet software
Data visualization tools and presentation software
, What do data analytics teams do in the operationalize phase of a data analytics
project?
• Apply data transformations to fix problems with data and surface
information
• Communicate project benefits,
• set up the pilot project, and deploy in production
• Explore data, create model
sets, and partition them into training, validation, and test sets
Translate business problems into data mining problems and locate appropriate
data Communicate project benefits, set up the pilot project, and deploy in
production What is the primary purpose of the operationalize phase in a data
analytics project?
• To pilot the model, refine it, and fully deploy it
• To develop and train various data models
• To prepare and clean the data for analysis
• To explore data and partition it into training, validation, and test sets
To pilot the model, refine it, and fully deploy it
What should business users and project sponsors do with their findings during the
operationalize phase of a data analytics project?
• Develop and refine data models
• Assess benefits, implications, and business impact
• Produce detailed reports and visuals
• Evaluate project completion and goals
Assess benefits, implications, and business impact
(2025/2026) RANDOMIZED QUESTION ORDER VERIFIED
A+ EXAM SOLUTIONS UNIQUE STUDY FORMAT
A healthcare company wants to predict which patients are at risk of developing a
certain medical condition. Which model is commonly used for this type of
analysis?
• Decision tree
• Association rules
• K-means clustering
• Logistic regression
Logistic regression
Logistic regression is a model that predicts the probability of an event occurring.
During a data analytics project, which phase focuses on developing training and
test datasets, refining models, and assessing the validity and predictive power of
the models?
• Model execution
• Data preparation
• Model planning
• Operationalize
Model execution
What is the main purpose of the model execution phase in a data analytics
project?
• To clean, transform, and aggregate data for analysis
,• To develop datasets, refine models, and assess validity
• To select appropriate models based on project goals
• To deploy the model and calculate its financial impact
To develop datasets, refine models, and assess validity
Which activities should the data analytics team perform during the model
execution phase of this project?
• Creating data visualizations and capturing essential predictors
• Deploying the model and measuring its return on investment
• Generating training and test sets and refining models to enhance
performance
• Grouping categorical variables and standardizing numeric values
Generating training and test sets and refining models to enhance performance
Which tool is suitable for a data analytics team to use during the model execution
phase of a project?
• SAS Enterprise
• Miner Tableau
• KNIME
• Microsoft Excel
SAS Enterprise Miner
Which phase of a data analytics project involves articulating findings and
outcomes for stakeholders while considering caveats, assumptions, and
limitations?
• Data preparation
• Communicate results
• Operationalize
,• Model development
Communicate results
What is the purpose of the communicate results phase in a data analytics project?
• Presenting findings and outcomes to stakeholders
• Preparing and managing data for analysis
• Evaluating the project's financial and technical results
• Creating and refining analytical models
Presenting findings and outcomes to stakeholders
Which activity should the data analytics team focus on during the communicate
results phase
• Presenting key findings to stakeholders and evaluating the project's success
• Building and testing different predictive models for customer churn
• Analyzing the financial impact of the project on the company's revenue and
customer retention
• Performing data cleaning and transforming raw data into usable formats
Presenting key findings to stakeholders and evaluating the project's success
Which tools are commonly used for communicating results in data analytics
projects?
• Predictive modeling software and programming languages
• Data visualization tools and presentation software
• Database management systems and data warehouses
• Text editors and spreadsheet software
Data visualization tools and presentation software
, What do data analytics teams do in the operationalize phase of a data analytics
project?
• Apply data transformations to fix problems with data and surface
information
• Communicate project benefits,
• set up the pilot project, and deploy in production
• Explore data, create model
sets, and partition them into training, validation, and test sets
Translate business problems into data mining problems and locate appropriate
data Communicate project benefits, set up the pilot project, and deploy in
production What is the primary purpose of the operationalize phase in a data
analytics project?
• To pilot the model, refine it, and fully deploy it
• To develop and train various data models
• To prepare and clean the data for analysis
• To explore data and partition it into training, validation, and test sets
To pilot the model, refine it, and fully deploy it
What should business users and project sponsors do with their findings during the
operationalize phase of a data analytics project?
• Develop and refine data models
• Assess benefits, implications, and business impact
• Produce detailed reports and visuals
• Evaluate project completion and goals
Assess benefits, implications, and business impact
