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AI Lifecycle Risk ManagementhardMultiple SelectObjective-mapped

AAIR AI Lifecycle Risk Management Practice Question

Which THREE factors should be monitored to detect 'Model Drift' in the deployment phase?

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

The distribution of prediction outputs.

Feature distribution, target distribution, and model confidence scores all provide signals of 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 distribution of prediction outputs.

    Why this is correct

    Detects if the model's behavior is changing significantly.

  • The temperature of the server room.

    Why it's wrong here

    Facility risk, not drift risk.

  • The current market price of GPU hardware.

    Why it's wrong here

    Financial, not drift risk.

  • The confidence score of the model predictions.

    Why this is correct

    Declining confidence often precedes performance failure.

  • The distribution of incoming input features.

    Why this is correct

    Detects if the incoming data population has shifted.

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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.