MLA-C01 Practice Question: ML Solution Monitoring, Maintenance, and Security
A data scientist uses SageMaker Model Monitor to track feature attribution drift. Which technique does SageMaker Model Monitor use to compute feature attributions?
⚠ Common exam trap
AWS often tests the misconception that SageMaker Model Monitor uses LIME for explainability because LIME is a popular model-agnostic method, but the service is specifically designed around SHAP for its theoretical properties and integration with the Amazon SageMaker Clarify framework.
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
✓
SHAP
SageMaker Model Monitor uses SHAP (SHapley Additive exPlanations) to compute feature attributions for model explainability and drift detection. SHAP provides a unified measure of feature importance based on cooperative game theory, ensuring consistent and locally accurate attributions across all features.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Permutation Feature Importance
Why it's wrong here
Permutation Feature Importance is a model-agnostic technique that measures prediction change when a feature is shuffled, and Model Monitor's feature attribution drift uses the SageMaker Clarify SHAP baseline instead. It is tempting because it also ranks feature influence, and it would be correct for generic model interpretation, but not for this monitor's attribution computation.
- ✓
SHAP
Why this is correct
SageMaker Model Monitor computes feature attributions using SHAP (SHapley Additive exPlanations), which satisfies the requirement for tracking feature attribution drift. The monitor runs a baseline against captured endpoint data, applying SHAP to quantify each feature's contribution, then compares distributions to detect drift in attribution values.
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Partial Dependence Plots
Why it's wrong here
Partial Dependence Plots show how predictions respond to a feature's value across its range, not per-instance attribution scores, so they cannot feed attribution drift. They are tempting for interpreting feature effects, and would be correct for visualising model behaviour, but Model Monitor computes attributions using SageMaker Clarify SHAP values.
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LIME
Why it's wrong here
SageMaker Model Monitor's feature attribution drift uses SHAP, which computes Shapley values from cooperative game theory. LIME builds local surrogate models around individual predictions, so it produces per-instance explanations rather than the baseline attribution baseline Model Monitor requires. LIME would suit ad-hoc local interpretability, not continuous drift monitoring.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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