MLA-C01 Practice Question: ML Solution Monitoring, Maintenance, and Security
A company uses SageMaker Model Monitor to track feature attribution drift with SHAP. They notice that the SHAP values have changed significantly for a feature, while the model performance remains stable. What is the MOST likely interpretation?
⚠ Common exam trap
Candidates often confuse feature attribution drift (SHAP drift) with data drift, assuming a change in SHAP values must be caused by a change in the input data distribution, when in fact it indicates a change in the model's learned behavior that can occur independently of data drift.
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
✓
The model's behavior has changed, which may lead to future performance degradation and warrants investigation
A significant change in SHAP values indicates that the model's internal feature importance has shifted, even if overall performance metrics like accuracy or loss remain stable. This is a classic sign of concept drift or model behavior drift, where the model's decision boundary has changed for that feature, which can lead to future performance degradation as the drift accumulates. SageMaker Model Monitor tracks feature attribution drift separately from data drift, and a change in SHAP values without data drift suggests the model is relying on the feature differently, warranting investigation.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The ground truth labels are incorrect, causing the drift
Why it's wrong here
SHAP drift is based on model predictions vs. baseline, not ground truth.
- ✗
Data drift has occurred for that feature
Why it's wrong here
SHAP drift is about attribution, not the feature distribution itself.
- ✗
The model is learning equally from all features, so no action is needed
Why it's wrong here
A significant change in SHAP values indicates a shift in feature importance, not equal learning.
- ✓
The model's behavior has changed, which may lead to future performance degradation and warrants investigation
Why this is correct
Feature attribution drift often precedes concept drift; it should be investigated.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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