A company is using SageMaker Autopilot to automatically build a binary classification model. After the AutoML job completes, the data scientist wants to understand which features are most important for the best candidate model. How can the scientist get feature importance?
Autopilot provides feature importance in the explainability tab for the best candidate.
Why this answer
SageMaker Autopilot automatically generates a 'Explainability' tab within the job details for the best candidate model. This tab uses SHAP (SHapley Additive exPlanations) values to provide feature importance, showing which features most influence the model's predictions. The data scientist can directly access this information without any additional configuration or re-running the model.
Exam trap
AWS often tests the misconception that feature importance must be manually extracted via code or logs, when in fact SageMaker Autopilot provides it directly in the UI under the 'Explainability' tab for the best candidate model.
How to eliminate wrong answers
Option B is wrong because SageMaker built-in XGBoost does not have a 'feature_importance' hyperparameter; feature importance is a property of the trained model object (e.g., via `get_fscore()` or `plot_importance()`), not a hyperparameter set before training. Option C is wrong because CloudWatch Logs for the training job contain training metrics, loss values, and algorithm logs, but not structured feature importance data; feature importance is not emitted to logs by default. Option D is wrong because SageMaker Ground Truth is a data labeling service for creating labeled datasets, not for extracting feature importance from a trained model; it is unrelated to model interpretability.