Model Planning - Describe Details of This Phase, Including Activities and Associated Roles DATA-SCIENCE-FOUNDATIONS Practice Questions (Page 4)

Questions 16–20

1. 16application · medium

   A retail company has completed data preparation and now needs to predict customer churn. The data science team has a prepared dataset with historical purchase behavior and customer demographics. During the model planning phase, the team must decide which modeling technique to use. Which action is most appropriate for the model planning phase?

   • ADeploy the chosen model to a production environment and monitor its performance in real time.
   • BCollect additional customer survey data to enrich the dataset before any modeling decisions are made.
   • CSelect a classification algorithm, such as logistic regression, and define evaluation metrics like accuracy and recall.
   • DEvaluate the prepared dataset for missing values and outliers, then impute them before modeling.
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2. 17application · medium

   A healthcare provider is planning a model to predict patient no-shows. The team has prepared appointment data and defined the business goal. During model planning, which activity is most critical?

   • ADeploying the model to the appointment scheduling system.
   • BCollecting additional patient feedback data.
   • CDefining the model objective and selecting a classification technique.
   • DBuilding the model using the prepared appointment data.
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4. 18foundation · easy

   Which of the following is a typical input to the model planning phase?

   • AThe production monitoring dashboard showing model drift.
   • BThe stakeholder presentation summarizing the project results.
   • CThe prepared dataset and the documented business requirements.
   • DThe final deployed model and its production performance metrics.
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5. 19application · medium

   An insurance company is planning a model to predict claim fraud. The team includes a data scientist, a business analyst, and a data engineer. During model planning, which task is the business analyst most likely to lead?

   • ASelecting the anomaly detection algorithm for the model.
   • BDefining the business objective and success criteria, such as reducing false positives.
   • CPreparing the claims data for modeling.
   • DWriting the code to train the fraud detection model.
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6. 20application · medium

   A financial services firm is entering the model planning phase for a fraud detection model. The team has prepared transaction data and documented business requirements. Which output is expected from the model planning phase?

   • AA finalized model that is ready for production deployment.
   • BA dashboard that visualizes fraud trends for business stakeholders.
   • CA model design document that specifies the algorithm and evaluation criteria.
   • DA cleaned and normalized dataset ready for analysis.
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