MLA-C01 ML Model Development Practice Question
A company uses SageMaker Autopilot to build a regression model predicting house prices. After the experiment completes, the company wants to understand why the model makes certain predictions. Which TWO SageMaker features can provide this explainability? (Choose TWO.)
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 Autopilot automatically generates explainability reports. SageMaker Clarify can be used separately for additional analysis. Model Monitor is for drift detection, not explainability. Debugger is for debugging training. Experiments is for tracking.
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 Clarify
Why this is correct
Clarify provides feature importance and SHAP values for model explainability.
- ✓
SageMaker Autopilot explainability report
Why this is correct
Autopilot generates an automatic explainability report with feature importance.
- ✗
SageMaker Model Monitor
Why it's wrong here
Model Monitor detects data drift and quality issues, not explainability.
- ✗
SageMaker Debugger
Why it's wrong here
Debugger monitors training for anomalies like overfitting, not post-training explainability.
- ✗
SageMaker Experiments
Why it's wrong here
Experiments track hyperparameters and metrics, not model interpretability.
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