AAIR AI Lifecycle Risk Management Practice Question
You are utilizing Azure Machine Learning to manage a deployment. You detect a sudden drop in model performance due to 'concept drift'. Which specific configuration in the Azure ML Model Monitoring dashboard should be adjusted to better detect this?
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
✓
Adjust the feature drift sensitivity threshold for categorical variables.
Concept drift occurs when the relationship between input variables and target variables changes, requiring adjustments to data drift detection parameters.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the retraining frequency of the pipeline.
Why it's wrong here
Retraining might mask the drift without identifying the root cause.
- ✗
Disable the data lineage capture to improve latency.
Why it's wrong here
Disabling lineage reduces visibility into risk, it does not fix detection.
- ✓
Adjust the feature drift sensitivity threshold for categorical variables.
Why this is correct
Concept drift often manifests as changes in the distribution of target variables relative to inputs, requiring sensitive drift monitoring.
- ✗
Switch the model to a higher-capacity compute instance.
Why it's wrong here
Compute capacity does not impact the logical drift of the model.
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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 AAIR 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 AAIR exam.