Model Planning - Describe Details of This Phase, Including Activities and Associated Roles DATA-SCIENCE-FOUNDATIONS Practice Questions (Page 3)

Questions 11–15

1. 11application · medium

   A utility company is planning a model to predict energy consumption. The team has prepared historical usage data and documented the business requirement to optimize grid load. Which output is expected from the model planning phase?

   • AA cleaned dataset with no missing values.
   • BA report on the model's performance in production.
   • CA trained model that can predict future consumption.
   • DA model design that specifies the regression technique and evaluation metric like RMSE.
   Reveal Solution Discussion Select an answer first
2. 12application · medium

   A telecom company is at the start of the model planning phase for a customer retention model. The project manager asks the team to clarify what will be accomplished in this phase. Which outcome is most aligned with the model planning phase?

   • AA fully trained model that is ready for validation.
   • BA dashboard showing customer churn trends.
   • CA cleaned dataset that is ready for analysis.
   • DA detailed plan for the modeling approach, including techniques and evaluation methods.
   Reveal Solution Discussion Select an answer first
4. 13expert · hard

   A data science team is planning a model for a new product launch. The business analyst wants to include a new data source that is not yet prepared. The data engineer says it will take two weeks to prepare. The data scientist wants to start modeling immediately with the existing prepared data. What is the best decision during model planning?

   • AStart building the model immediately without a plan.
   • BProceed with the existing prepared data and plan to incorporate the new data later if needed.
   • CAbandon the new data source and use only the existing data.
   • DWait for the new data to be prepared before planning the model.
   Reveal Solution Discussion Select an answer first
5. 14expert · hard

   A data science team is planning a model to predict customer churn. The business analyst emphasizes that the cost of false positives is high because retention offers are expensive. The data scientist suggests using a complex ensemble method, but the data engineer warns that the prepared dataset is small and imbalanced. Which approach best balances the business constraint and data limitations during model planning?

   • ASelect a simpler model like logistic regression and define precision as the primary evaluation metric.
   • BUse a complex ensemble method and prioritize recall to catch all churners.
   • CSelect a simple model and use recall as the primary metric to maximize churn detection.
   • DChoose a complex ensemble method and use accuracy as the primary metric.
   Reveal Solution Discussion Select an answer first
6. 15foundation · easy

   Which of the following is a key activity performed during the model planning phase?

   • AWriting the production code that trains the model on the full dataset.
   • BCleaning missing values and removing duplicate records from the dataset.
   • CSelecting the modeling technique, such as regression or classification, based on the business question.
   • DPublishing the final results to a stakeholder presentation.
   Reveal Solution Discussion Select an answer first