- A
Revisit training data for historical bias and consider reweighting
Addressing data bias is a fundamental step to reduce disparate impact.
- B
Ignore the disparity because the model is accurate overall
Why wrong: Ignoring disparity may violate anti-discrimination laws.
- C
Remove all demographic attributes from the dataset
Why wrong: Removing attributes may not eliminate bias due to proxy variables, and can hinder fairness analysis.
- D
Apply fairness constraints or adversarial debiasing during training
Fairness-aware training algorithms can directly reduce bias.
- E
Consider using a different model that achieves better fairness metrics
Switching to a fairer model is a viable mitigation strategy.
Question 986 of 1,000
AI0-001 AI Governance and Ethics Practice Question
This AI0-001 practice question tests your understanding of ai governance and ethics. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A company uses an AI model to screen job applicants. A disparate impact analysis reveals that the model's rejection rate for a protected group is significantly higher than for others. Which THREE actions should the company take to address this?
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
✓
Revisit training data for historical bias and consider reweighting
Option A is correct because revisiting the training data for historical bias and applying reweighting directly addresses the root cause of disparate impact. If the training data contains biased labels or skewed representation of the protected group, the model will learn and amplify those biases. Reweighting adjusts the loss function to give more importance to underrepresented or disadvantaged groups, helping to equalize error rates across groups.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Revisit training data for historical bias and consider reweighting
Why this is correct
Addressing data bias is a fundamental step to reduce disparate impact.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Ignore the disparity because the model is accurate overall
Why it's wrong here
Ignoring disparity may violate anti-discrimination laws.
- ✗
Remove all demographic attributes from the dataset
Why it's wrong here
Removing attributes may not eliminate bias due to proxy variables, and can hinder fairness analysis.
- ✓
Apply fairness constraints or adversarial debiasing during training
Why this is correct
Fairness-aware training algorithms can directly reduce bias.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Consider using a different model that achieves better fairness metrics
Why this is correct
Switching to a fairer model is a viable mitigation strategy.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that removing protected attributes (option C) is sufficient to eliminate bias, when in reality it can hide bias and still allow proxy discrimination, making it an incomplete and sometimes counterproductive solution.
Detailed technical explanation
How to think about this question
Disparate impact is typically measured using the 80% rule or statistical parity difference; reweighting works by assigning sample weights inversely proportional to group representation or by using importance sampling to balance the loss gradient. Adversarial debiasing, another correct technique, trains a classifier to maximize accuracy while an adversary tries to predict the protected attribute from the model's predictions, forcing the model to learn representations that are invariant to the protected attribute. In practice, combining data reweighting with adversarial debiasing often yields the best fairness-accuracy trade-off.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A practitioner preparing for the AI0-001 exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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FAQ
Questions learners often ask
What does this AI0-001 question test?
AI Governance and Ethics — This question tests AI Governance and Ethics — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Revisit training data for historical bias and consider reweighting — Option A is correct because revisiting the training data for historical bias and applying reweighting directly addresses the root cause of disparate impact. If the training data contains biased labels or skewed representation of the protected group, the model will learn and amplify those biases. Reweighting adjusts the loss function to give more importance to underrepresented or disadvantaged groups, helping to equalize error rates across groups.
What should I do if I get this AI0-001 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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Last reviewed: Jul 4, 2026
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.
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