AIF-C01 Fundamentals of AI and ML Practice Question
A healthcare company is using Amazon SageMaker to deploy a model that makes predictions on patient data. They need to ensure that the model's predictions are explainable to comply with regulations. Which approach should they take?
⚠ Common exam trap
The AIF-C01 exam often tests the distinction between monitoring (Model Monitor), tracking (Experiments), debugging (Debugger), and explainability (Clarify), so the trap here is confusing operational monitoring with the need for interpretable explanations required by compliance frameworks.
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 SageMaker Clarify to generate feature importance and explanations
SageMaker Clarify is specifically designed to provide model explainability, including feature importance and SHAP-based explanations, which are essential for regulatory compliance in healthcare. It helps stakeholders understand why a model made a particular prediction, addressing transparency requirements.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use SageMaker Model Monitor to track predictions
Why it's wrong here
Model Monitor detects data drift, not explainability.
- ✗
Use SageMaker Experiments to log model parameters
Why it's wrong here
Experiments track runs, not explainability.
- ✓
Use SageMaker Clarify to generate feature importance and explanations
Why this is correct
Clarify provides model explainability, including SHAP and partial dependence plots.
- ✗
Use SageMaker Debugger to analyze training gradients
Why it's wrong here
Debugger monitors training, not inference explanations.
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