AI0-001 AI Implementation and Operations Practice Question
A team monitors a production model for bias. They measure the selection rate for two demographic groups and find a significant difference. Which TWO actions should the team take to mitigate bias? (Choose two.)
⚠ Common exam trap
CompTIA often tests the misconception that removing the protected attribute (Option D) is sufficient to eliminate bias, when in reality proxy features and correlated variables can perpetuate discrimination.
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
✓
Retrain the model with a balanced training dataset
Retraining with a balanced training dataset (Option C) directly addresses the root cause of bias by ensuring the model learns from equal representation of both demographic groups, which reduces skewed selection rates. This is a standard data-level mitigation technique in AI fairness, as it prevents the model from overfitting to majority patterns.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the complexity of the model to capture more patterns
Why it's wrong here
Increasing model complexity does not address the measured selection-rate disparity; it risks amplifying existing biases by fitting noise rather than enforcing fairness constraints. This option is tempting because adding complexity can improve predictive accuracy on imbalanced data, which would be correct if the goal were reducing underfitting rather than mitigating demographic bias.
- ✗
Add more training data from both groups
Why it's wrong here
Adding more data without balancing may not reduce bias.
- ✓
Retrain the model with a balanced training dataset
Why this is correct
Balanced data reduces bias by ensuring the model learns from fair representations.
- ✗
Remove the protected attribute from the model input
Why it's wrong here
Removing the attribute may not eliminate bias due to proxy features.
- ✓
Implement a post-processing fairness adjustment
Why this is correct
Post-processing techniques adjust predictions to achieve fairness metrics.
About these practice questions
Courseiva writes every AI0-001 question from scratch — 754 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 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.