Objective 2.5 Address Business Risks, Ethical Concerns, and Related Concepts in Data Exploration/feature Engineering AIP-210 Practice Questions (Page 7)

Questions 31–32

1. 31application · medium

   A retail company is building a churn-prediction model. During feature engineering, the team notices that the 'customer age' feature has a high proportion of missing values for a specific demographic segment. They are considering imputation methods. Which practice aligns with responsible AI principles?

   • AImpute missing ages using the median age of the entire customer base and add a flag indicating imputation was applied.
   • BDrop all rows with missing age values to ensure the model only trains on complete, high-quality data.
   • CInvestigate the reason for missingness before choosing an imputation strategy, and document the decision for auditability.
   • DUse a complex model-based imputation that predicts age from other features without further analysis.
   Reveal Solution Discussion Select an answer first
2. 32application · medium

   A data science team is building a model for a non-profit organization to predict donation likelihood. The team has access to a dataset that includes donor names, addresses, and donation history. The non-profit wants to ensure the data is used responsibly. What is the most important practice to implement during feature engineering?

   • AUse as many features as possible to maximize model accuracy.
   • BAvoid documenting the feature engineering process to save time.
   • CShare the raw dataset with all team members for maximum collaboration.
   • DDocument the purpose of each feature and how it relates to the model's objective.
   Reveal Solution Discussion Select an answer first