AAISM AI Risk Management Practice Question
A machine learning model is showing signs of 'data drift'. What is the most effective initial step in the risk assessment process?
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
✓
Analyze the statistical distribution of input production data versus training data.
Data drift monitoring involves comparing current production data distributions against training data distributions to detect performance decay.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Retrain the model immediately with all available data.
Why it's wrong here
Blind retraining can introduce bias or noise if the root cause of drift is not understood.
- ✓
Analyze the statistical distribution of input production data versus training data.
Why this is correct
Statistical analysis is the baseline step for confirming and scoping data drift.
- ✗
Shut down the application to prevent incorrect predictions.
Why it's wrong here
Disabling the system is a last resort, not the initial assessment step.
- ✗
Change the model architecture to a more complex design.
Why it's wrong here
Complexity usually increases risk and does not address data drift.
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 AAISM 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 AAISM exam.