AAIR AI Lifecycle Risk Management Practice Question
A team is designing an AI lifecycle monitoring system. Which metric is most effective for detecting 'Out-of-Distribution' (OOD) risks?
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
✓
Anomaly scores derived from the model's embedding space or activation layers.
OOD detection identifies inputs that are fundamentally different from the distribution the model was trained on.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Anomaly scores derived from the model's embedding space or activation layers.
Why this is correct
Inputs that produce unusual activations in the internal layers suggest the model is processing data it doesn't recognize.
- ✗
Total number of users accessing the model.
Why it's wrong here
User count is a business metric.
- ✗
Average request latency.
Why it's wrong here
Latency is irrelevant to input distribution.
- ✗
The number of lines of code in the model file.
Why it's wrong here
Code size is not related to OOD detection.
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 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.