Describe the Data Analytics Lifecycle Purpose and Sequence of Phases DATA-SCIENCE-FOUNDATIONS Practice Questions (Page 3)

Questions 11–15

1. 11foundation · easy

   What is the primary activity in the Data Preparation phase of the data analytics lifecycle?

   • APresenting findings and insights to stakeholders.
   • BSelecting the appropriate modeling techniques and algorithms.
   • CCleaning, transforming, and organizing data to make it suitable for analysis.
   • DDefining the business problem and project objectives.
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2. 12expert · hard

   A financial services company is in the model building phase of a credit risk project. The team has built a model that performs well on historical data, but during validation, they discover that the model's performance degrades significantly on new data due to changes in economic conditions. What should the team do according to the data analytics lifecycle?

   • AIgnore the performance degradation and proceed to communicating results.
   • BSkip to operationalization and monitor the model's performance in production.
   • CReturn to the model planning phase to reconsider the approach and possibly select a different algorithm.
   • DDeploy the model immediately because it performed well on historical data.
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4. 13application · medium

   A bank is using the data analytics lifecycle for a fraud detection project. The team has identified the business problem, inventoried data sources, and interviewed stakeholders. Which phase have they just completed?

   • AModel planning
   • BDiscovery
   • COperationalization
   • DData preparation
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5. 14expert · hard

   A manufacturing company has a tight deadline to deliver insights from sensor data to reduce equipment downtime. The team is considering skipping the data preparation phase to save time, arguing that the sensor data is already clean. However, the data comes from multiple sensor types with different formats and some missing timestamps. What is the most appropriate action?

   • ADefer the project until more time is available for full data preparation.
   • BProceed to model building and address data issues during operationalization.
   • CPerform a minimal data preparation pass to standardize formats and handle missing timestamps.
   • DSkip data preparation and proceed directly to model building to meet the deadline.
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6. 15expert · hard

   A healthcare analytics team is working on a project to predict patient no-shows. They have completed the data preparation phase and are now in model planning. During model planning, they realize that the available data is insufficient to train a reliable model. What should the team do according to the data analytics lifecycle?

   • AProceed to model building with the available data to see what happens.
   • BDeploy the model immediately to start generating predictions.
   • CSkip model planning and move directly to communicating results.
   • DReturn to the discovery phase to identify additional data sources.
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