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?
⚠ Common exam trap
The trap is conflating data drift (change in input distribution P(X)) with concept drift (change in the relationship P(Y|X)) — the question deliberately states inputs are unchanged to force you to recognize concept 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
✓
Concept drift in the model
Feature attribution drift monitoring compares SHAP value distributions between baseline and current data. If the input distribution of a feature is unchanged but its average SHAP value shifts significantly, the model's learned relationship between that feature and the target has changed — this is the definition of concept drift. The model itself is unchanged, but the underlying mapping from inputs to outputs in the real world has shifted, so the same input values now contribute differently to 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.
- ✗
The feature is no longer relevant to predictions
Why it's wrong here
An increased average SHAP magnitude means the feature contributes more to predictions, not less; irrelevance would show attribution shrinking toward zero. Confusing magnitude with importance is tempting because both concern a feature's role, but the alert direction indicates growing influence, typically from concept drift or a retrained model.
- ✗
A bug in the SHAP computation
Why it's wrong here
A systematic SHAP bug would corrupt all features' attributions, not one feature's average value while inputs remain stable. Suspecting a computation fault is tempting because SHAP values are model-derived and can look opaque, yet a genuine change in the model's learned reliance on that feature explains the isolated increase.
- ✗
Data drift in that feature
Why it's wrong here
Feature attribution drift measures change in a feature's contribution to predictions, not change in its input values; the stem explicitly states the input distribution is unchanged, which is what data drift describes. Data drift monitoring is the right tool when input distributions themselves shift, but here the model's reliance on the feature has altered.
- ✓
Concept drift in the model
Why this is correct
Feature attribution drift with unchanged input distribution isolates the model's learned relationship: SHAP values rising while inputs stay stable means the model now weights that feature differently, which is concept drift rather than data drift.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
This MLA-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLA-C01 exam.