MLA-C01 ML Model Development Practice Question
A data scientist is using SageMaker Autopilot to automatically build a binary classification model on a balanced dataset. They want to understand the relationship between the input features and the model predictions. Which feature in SageMaker Autopilot should they use?
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
✓
Explainability reports
SageMaker Autopilot generates explainability reports, including feature importance and model insights, via the 'Explainability' feature. This provides the relationship between features and predictions.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Explainability reports
Why this is correct
Explainability reports provide feature importance and SHAP values, showing how features impact predictions.
- ✗
Data visualizations
Why it's wrong here
Data visualizations show data distributions, not model-specific feature relationships.
- ✗
Model tuning results
Why it's wrong here
Tuning results show hyperparameter combinations and performance metrics, not feature relationships.
- ✗
Model candidate definitions
Why it's wrong here
Candidate definitions describe the pipelines, not feature importance.
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