AIF-C01 Guidelines for Responsible AI Practice Question

A healthcare startup deploys a model to predict patient readmission risk using Amazon SageMaker. After deployment, the model shows higher false-positive rates for a specific age group. What is the most responsible first step?

⚠ Common exam trap

AWS often tests the misconception that the first step to address bias is to immediately retrain or adjust thresholds, rather than using a dedicated bias detection tool like SageMaker Clarify to first diagnose the nature and extent of the bias.

Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

✓

Use Amazon SageMaker Clarify to detect bias in predictions

Amazon SageMaker Clarify is purpose-built for detecting bias in ML models and data. It provides bias metrics (e.g., Difference in Positive Proportions in Predicted Labels, Disparate Impact) that can quantify whether the model's predictions are systematically skewed against a specific age group. This is the most responsible first step because it objectively measures the bias before any corrective action is taken.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✗
Increase the prediction threshold for the affected group
Why it's wrong here
Adjusting thresholds can mask bias.
✓
Use Amazon SageMaker Clarify to detect bias in predictions
Why this is correct
Clarify provides bias metrics to inform next steps.
✗
Retrain the model with more data from the affected group
Why it's wrong here
Without analysis, retraining may not fix bias.
✗
Immediately retire the model to prevent harm
Why it's wrong here
Premature retirement may disrupt operations.

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AIF-C01 Guidelines for Responsible AI Practice Question

Why each option matters

Option-by-option breakdown

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