AAIR AI Lifecycle Risk Management Practice Question
Which TWO of the following techniques help mitigate bias in the AI lifecycle?
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
✓
Conducting fairness audits using specialized metrics (e.g., Disparate Impact).
Diverse data sourcing and fairness evaluation are standard methods for bias mitigation.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Switching the programming language to Java.
Why it's wrong here
Language choice has no effect on model bias.
- ✗
Reducing the frequency of model monitoring.
Why it's wrong here
Less monitoring increases risk.
- ✓
Conducting fairness audits using specialized metrics (e.g., Disparate Impact).
Why this is correct
Provides quantitative evidence of bias.
- ✓
Sourcing training data from diverse, representative populations.
Why this is correct
Addresses bias at the source.
- ✗
Increasing the number of features in the model.
Why it's wrong here
More features can often lead to more bias, not less.
About these practice questions
This AAIR question is part of Courseiva's 199-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam 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 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.