AIF-C01 Guidelines for Responsible AI Practice Question

A research lab uses Amazon SageMaker to train a deep learning model for medical diagnosis. They need to ensure the model's decisions are interpretable to clinicians. Which SageMaker feature provides local and global feature importance?

⚠ Common exam trap

It's easy for candidates to confuse SageMaker Debugger's ability to monitor training metrics with model interpretability, but Debugger does not compute feature importance or explain predictions.

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

✓

SageMaker Clarify

SageMaker Clarify is the correct answer because it is specifically designed to provide both local and global feature importance for machine learning models. Local feature importance explains individual predictions (e.g., why a specific patient was diagnosed), while global feature importance shows which features most influence the model overall. This directly supports interpretability for clinicians, as required in the question.

Answer analysis

Option-by-option breakdown

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

✗
SageMaker Model Monitor
Why it's wrong here
Model Monitor detects drift.
✗
SageMaker Experiments
Why it's wrong here
Experiments organizes model iterations.
✓
SageMaker Clarify
Why this is correct
Clarify provides explainability metrics.
✗
SageMaker Debugger
Why it's wrong here
Debugger helps find training issues.

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AIF-C01 Guidelines for Responsible AI Practice Question

Why each option matters

Option-by-option breakdown

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