AI0-001 AI Security, Ethics and Governance Practice Question
Which TWO of the following are common methods for mitigating bias in AI models?
⚠ Common exam trap
CompTIA often tests the distinction between bias mitigation techniques (pre-processing, in-processing, post-processing) and general ML best practices like regularization or cross-validation, leading candidates to confuse L1 regularization or k-fold cross-validation with fairness methods.
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
✓
Reweighting training samples based on sensitive attributes
Reweighting training samples based on sensitive attributes is a common pre-processing bias mitigation technique. It assigns higher weights to underrepresented groups or lower weights to overrepresented groups to balance the dataset, thereby reducing the model's reliance on biased correlations. This method directly addresses data-level bias before model training begins.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Using adversarial training
Why it's wrong here
Adversarial training improves robustness to adversarial examples, not bias.
- ✓
Reweighting training samples based on sensitive attributes
Why this is correct
Reweighting can adjust for underrepresented groups to reduce bias.
- ✗
Applying L1 regularization
Why it's wrong here
L1 regularization induces sparsity, not fairness.
- ✓
Adding fairness constraints during training
Why this is correct
Fairness constraints directly enforce fairness during model training.
- ✗
Performing k-fold cross-validation
Why it's wrong here
Cross-validation assesses performance, does not mitigate bias.
About these practice questions
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI0-001 practice question is part of Courseiva's free CompTIA 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 AI0-001 exam.