MLA-C01 ML Model Development Practice Question
A company wants to use SageMaker Autopilot to automatically build a binary classification model. Which output does Autopilot provide to help understand model decisions?
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
✓
An explainability report with feature importance
SageMaker Autopilot generates an explainability report with feature importance. It does not provide a confusion matrix by default; users must evaluate separately. It does not provide SHAP values directly but uses similar techniques. Leaderboard is for ranking trials, not explainability.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
A leaderboard of models with only accuracy metrics
Why it's wrong here
Leaderboard includes multiple metrics but not explainability.
- ✓
An explainability report with feature importance
Why this is correct
Autopilot generates an explainability report as part of its output.
- ✗
A confusion matrix for each candidate model
Why it's wrong here
Autopilot does not automatically output confusion matrices; users must generate them.
- ✗
A SHAP values summary plot for each trial
Why it's wrong here
Autopilot uses feature importance but not necessarily SHAP plots.
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