AAISM AI Risk Management Practice Question
Which TWO techniques help mitigate 'Model Poisoning'?
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
✓
Data sanitization and filtering of training inputs.
Defensive techniques like data sanitization and robust aggregation algorithms help mitigate poisoning attempts.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Data sanitization and filtering of training inputs.
Why this is correct
Filtering out anomalies prevents malicious data from entering the training set.
- ✗
Increasing the number of neurons in the model.
Why it's wrong here
This does not protect against poisoning.
- ✓
Using robust statistical aggregation for training data.
Why this is correct
Robust aggregation reduces the impact of outliers/poisoned samples.
- ✗
Moving the model to a public cloud environment.
Why it's wrong here
This increases the attack surface.
- ✗
Reducing the number of training epochs.
Why it's wrong here
This does not address poisoning.
About these practice questions
Courseiva writes every AAISM question from scratch — 205 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
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.