AAIA AI Operations Practice Question
When implementing drift detection in AWS SageMaker Model Monitor, which metric should be monitored to detect feature attribution shift for structured data?
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
✓
Feature Attribution Drift (SHAP)
Feature attribution drift monitors how the model's reliance on specific input features changes over time.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Cosine Similarity
Why it's wrong here
Used for embedding drift, not attribution.
- ✗
Mean Square Error
Why it's wrong here
Measures prediction accuracy.
- ✓
Feature Attribution Drift (SHAP)
Why this is correct
This directly measures shift in SHAP values.
- ✗
Precision-Recall AUC
Why it's wrong here
Measures performance, not feature attribution.
About these practice questions
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed August 2026 · checked against the official ISACA exam blueprint
This AAIA practice question is part of Courseiva's free ISACA 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 AAIA exam.