AI0-001 AI Concepts and Foundations Practice Question
An AI governance committee is reviewing a resume-screening model. The model's accuracy is high overall, but its false negative rate is much higher for applicants from one demographic group than for others. The committee wants to address this disparity. Which action best targets the problem?
⚠ Common exam trap
The trap here is believing that deleting protected attributes makes a model fair, when proxy variables preserve the bias and hide it from measurement.
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
✓
Measure and mitigate bias across demographic groups, including error rates and representation
The scenario describes unequal false negative rates, a fairness problem that requires measuring performance by demographic group and mitigating the disparity through data or threshold adjustments. Adding data, removing protected attributes, or switching to a simpler algorithm may change accuracy or transparency but does not directly close the group-level error gap.
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 model's overall accuracy by adding more training data
Why it's wrong here
Higher aggregate accuracy does not guarantee fair error rates and can even widen gaps if new data reflects existing imbalances. The committee needs group-level measurement and targeted mitigation, not simply more data that may reproduce the same skew.
- ✗
Remove all demographic attributes from the training data
Why it's wrong here
Dropping protected attributes does not remove bias because correlated features such as zip code or school can act as proxies. Blindness to group membership also prevents the team from measuring disparate error rates, making the disparity harder to detect and correct.
- ✓
Measure and mitigate bias across demographic groups, including error rates and representation
Why this is correct
Disparate false negative rates across groups are a fairness and bias signal. Auditing error rates by group, checking training data representation, and applying mitigation such as reweighting or threshold adjustment directly addresses the observed disparity, whereas overall accuracy can hide unequal performance.
- ✗
Replace the model with a simpler algorithm that is inherently interpretable
Why it's wrong here
Interpretability aids explanation but does not by itself equalize error rates between groups. A simpler model can still inherit historical bias from its training labels, so swapping algorithms without measuring and mitigating group-level performance leaves the core disparity unaddressed.
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 September 2026 · checked against the official CompTIA exam blueprint
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.