WGU D491 INTRODUCTION TO ANALYTICS EXAM (2025/2026) RANDOM QUESTION & ANSWERS

WGU D491 INTRODUCTION TO ANALYTICS EXAM (2025/2026) RANDOM QUESTION & ANSWERS 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 What should analysts do with the findings discovered during the operationalize phase of a data analytics project? • Assess project risks and return on investment (ROI) • Create technical specifications • Evaluate the project's success • Modify reports and dashboards