- A
Use a different evaluation metric such as equalized odds
Why wrong: Changing metrics does not fix the disparity; it only changes what is measured.
- B
Remove the sensitive attribute from the training data
Why wrong: Removing the attribute may reduce direct discrimination but can still lead to disparate impact via proxy features.
- C
Collect more data for Group A to increase its representation
Why wrong: Collecting more data may help, but it does not guarantee equal selection rates without changing the model.
- D
Retrain the model with a fairness constraint that enforces demographic parity
Enforcing demographic parity during training directly addresses the disparate selection rates.
Question 203 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 data scientist is evaluating a binary classifier for a hiring tool. They compute demographic parity and find that the selection rate for Group A is 0.2 and for Group B is 0.4. Which action would MOST directly address this disparity?
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 fairness constraint that enforces demographic parity
Demographic parity requires equal selection rates. Retraining with a fairness constraint that enforces demographic parity directly adjusts the model to achieve equal rates. Rebalancing the dataset (if the disparity stems from imbalanced labels) might help, but it does not guarantee parity. Modifying thresholds can also achieve parity, but post-processing without retraining may degrade other metrics; retraining with constraint is more direct.
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.
- ✗
Use a different evaluation metric such as equalized odds
Why it's wrong here
Changing metrics does not fix the disparity; it only changes what is measured.
- ✗
Remove the sensitive attribute from the training data
Why it's wrong here
Removing the attribute may reduce direct discrimination but can still lead to disparate impact via proxy features.
- ✗
Collect more data for Group A to increase its representation
Why it's wrong here
Collecting more data may help, but it does not guarantee equal selection rates without changing the model.
- ✓
Retrain the model with a fairness constraint that enforces demographic parity
Why this is correct
Enforcing demographic parity during training directly addresses the disparate selection rates.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- 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.
Quick reference
RAID Level Comparison
| RAID Level | Min Disks | Fault Tolerance | Read | Write | Usable Capacity |
|---|---|---|---|---|---|
| RAID 0 | 2 | None | Excellent | Excellent | 100% |
| RAID 1 | 2 | 1 disk | Good | Moderate | 50% |
| RAID 5 | 3 | 1 disk | Good | Moderate | 67–94% |
| RAID 6 | 4 | 2 disks | Good | Lower | 50–88% |
| RAID 10 | 4 | 1 disk per mirror | Excellent | Good | 50% |
RAID is not a backup strategy — it protects against disk failure but not against accidental deletion, ransomware, or site-level events.
What to study next
Got this wrong? Here's your next step.
Identify which AI0-001 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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AI Governance and Ethics — study guide chapter
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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: Retrain the model with a fairness constraint that enforces demographic parity — Demographic parity requires equal selection rates. Retraining with a fairness constraint that enforces demographic parity directly adjusts the model to achieve equal rates. Rebalancing the dataset (if the disparity stems from imbalanced labels) might help, but it does not guarantee parity. Modifying thresholds can also achieve parity, but post-processing without retraining may degrade other metrics; retraining with constraint is more direct.
What should I do if I get this AI0-001 question wrong?
Identify which AI0-001 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
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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