AIF-C01 Fundamentals of AI and ML Practice Question

A financial services company needs to ensure that the machine learning models used for loan approval are explainable and meet regulatory compliance. Which AWS feature can help explain model predictions?

⚠ Common exam trap

Candidates often confuse monitoring (Model Monitor) with explainability (Clarify), as both relate to model governance but serve fundamentally different purposes—monitoring tracks performance over time, while Clarify explains individual 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 AWS service for explaining model predictions because it provides feature attribution and bias detection capabilities. It uses SHAP (SHapley Additive exPlanations) to generate explainability reports, which are essential for meeting regulatory compliance in financial services like loan approval.

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 Ground Truth
Why it's wrong here
Ground Truth is for data labeling.
✓
SageMaker Clarify
Why this is correct
Clarify provides feature importance, SHAP values, and bias metrics for model explainability.
✗
SageMaker Automatic Model Tuning
Why it's wrong here
AMT optimizes hyperparameters, not explainability.
✗
SageMaker Model Monitor
Why it's wrong here
Model Monitor detects drift, not explain predictions.

Go deeper

AIF-C01 Fundamentals of AI and ML Practice Question

Why each option matters

Option-by-option breakdown

Related to this question