AI0-001 AI Security, Ethics and Governance Practice Question
A startup develops an AI recruiting tool that screens resumes. After deployment, they receive a complaint from a candidate who claims the system rejected them due to age discrimination. The startup has no formal AI governance process. They want to quickly assess and remediate the issue. The dataset includes age as a feature. What should they do first?
⚠ Common exam trap
CompTIA often tests the misconception that removing a protected attribute (like age) is sufficient to eliminate bias, when in fact proxy features can perpetuate discrimination, making a bias analysis the necessary first step.
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
✓
Conduct a bias analysis to measure the model's impact on different age groups
The first step in addressing a potential bias issue is to conduct a bias analysis to measure the model's impact on different age groups. This allows the startup to quantify the extent of any discriminatory behavior before taking remediation steps, ensuring that actions are data-driven and targeted. Without this analysis, any subsequent fix (like removing age) might be premature or ineffective, and could even introduce new biases.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Conduct a bias analysis to measure the model's impact on different age groups
Why this is correct
Measuring outcomes across age groups establishes whether the model actually disadvantages older candidates before any remediation. Since age is a training feature, this analysis identifies the specific disparity and informs whether to remove the feature, reweight samples, or retrain.
- ✗
Apologize to the candidate and offer a manual review of their resume
Why it's wrong here
Apologising and offering a manual review addresses one candidate's outcome but leaves the age feature in the screening model, so the discriminatory behaviour persists for every future applicant. It is tempting because redress feels responsive, yet it is the correct step only after the model has been assessed and remediated.
- ✗
Immediately remove age from the feature set and retrain the model
Why it's wrong here
Removing age addresses one feature but leaves proxy variables such as graduation year, and retraining without auditing the data or model may hide the bias. This step is tempting as a quick fix, yet governance first requires measuring disparate impact across protected groups before remediation.
- ✗
Ignore the complaint because age is a legitimate business requirement
Why it's wrong here
Age is not a legitimate business requirement for resume screening; treating it as one leaves the biased feature in place and the discrimination ongoing. Ignoring the complaint is tempting when no governance process exists, but dismissal is defensible only where the attribute is a genuine occupational requirement, which age-based resume filtering is not.
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.