Objective 3.5 Address Business Risks, Ethical Concerns, and Related Concepts in Training and Tuning AIP-210 Practice Questions (Page 5)

Questions 21–25

1. 21foundation · easy

   When evaluating the ethical implications of a model's decisions, which of the following is a primary concern?

   • AThe model's ability to generalize to unseen data.
   • BThe model's inference speed on production hardware.
   • CThe model's decisions may disproportionately disadvantage a particular demographic group.
   • DThe model's training time and computational cost.
   Reveal Solution Discussion Select an answer first
2. 22expert · medium

   A large enterprise is deploying multiple ML models across different business units. The enterprise wants to ensure that all models are developed responsibly and can be audited. They also want to avoid duplication of effort and ensure consistency. Which governance framework should they implement?

   • AOutsource model development to external vendors to avoid internal governance issues.
   • BLet each business unit manage its own models independently to maximize flexibility.
   • CCreate a centralized ML governance team that defines standards, reviews models, and maintains a model registry.
   • DUse a single model for all business units to simplify governance.
   Reveal Solution Discussion Select an answer first
4. 23expert · medium

   A tech company is training a model on user browsing data to personalize ads. The data includes IP addresses and browsing history. The company must comply with GDPR and CCPA. They want to use differential privacy to protect user privacy. However, adding too much noise reduces ad relevance, impacting revenue. What should the company do?

   • AUse a large privacy budget (epsilon) to add more noise, ensuring strong privacy.
   • BStore the data on servers in a country with less strict privacy laws.
   • CAnonymize the data by removing IP addresses and use no differential privacy.
   • DUse a small privacy budget (epsilon) to add minimal noise, balancing privacy and utility.
   Reveal Solution Discussion Select an answer first
5. 24application · medium

   A healthcare provider is deploying a model to predict patient readmission risk. Regulators require that patients be able to understand why a decision was made. The data science team must provide a human-readable explanation for each individual prediction. Which method should they use?

   • ACompute permutation importance on the validation set to identify the most influential features overall.
   • BTrain a linear model and report the coefficients as feature importance.
   • CVisualize the model's decision boundary in a two-dimensional projection of the feature space.
   • DUse SHAP values to show how each feature contributed to a specific patient's risk score.
   Reveal Solution Discussion Select an answer first
6. 25application · medium

   An e-commerce company has deployed a product recommendation model. Six months later, they notice that the click-through rate has dropped significantly. The data distribution has also shifted because of a new product category. What should the ML team do first?

   • ARoll back to the previous model version to restore performance.
   • BInvestigate the model's performance metrics and data drift to confirm the cause of the degradation.
   • CIncrease the model's learning rate and retrain on the original data.
   • DRetrain the model immediately with the most recent data.
   Reveal Solution Discussion Select an answer first