MLA-C01 Practice Question: ML Solution Monitoring, Maintenance, and Security
A company uses SageMaker Model Monitor's feature attribution drift monitoring with SHAP. They receive an alert that the average SHAP value for a particular feature has increased significantly compared to the baseline. The feature's input distribution has not changed. What does this likely indicate?
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
✓
Concept drift in the model
SHAP values measure the contribution of each feature to the model's predictions. A change in SHAP values while input distribution is stable suggests the model has learned a new reliance on that feature, i.e., the relationship between the feature and the target has changed — concept drift.
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 feature is no longer relevant to predictions
Why it's wrong here
An increase in SHAP value suggests the feature has become more important, not less.
- ✗
A bug in the SHAP computation
Why it's wrong here
While possible, the most likely and actionable interpretation is concept drift.
- ✗
Data drift in that feature
Why it's wrong here
Data drift would manifest as a change in the input distribution, which is not observed here.
- ✓
Concept drift in the model
Why this is correct
SHAP attribution drift indicates that the model's reliance on features has changed, which is a sign of concept drift.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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