- A
The system violates individual fairness
Why wrong: Individual fairness compares similar individuals, not group error rates.
- B
The system suffers from selection bias
Why wrong: Selection bias is a data source issue, not necessarily indicated by differing false positive rates.
- C
The system violates equalised odds
Equalised odds demands equal false positive rates across groups; significant differences indicate unfairness.
- D
The system is not calibrated
Why wrong: Calibration compares predicted probabilities to actual outcomes, not group error rates.
Question 529 of 1,000
AI0-001 AI Governance and Ethics Practice Question
This AI0-001 practice question tests your understanding of ai governance and ethics. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 is evaluating a vendor's AI system for hiring. The vendor claims the system is fair because it achieves demographic parity. However, the company discovers that the system has significantly different false positive rates across groups. Which fairness issue does this indicate?
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
✓
The system violates equalised odds
Equalised odds requires that a model's false positive rates and true positive rates are equal across all demographic groups. Since the vendor's system has significantly different false positive rates across groups, it violates the equalised odds fairness criterion, even if demographic parity (equal selection rates) is satisfied. This is a core fairness metric in AI governance, as it ensures that errors are distributed equitably.
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.
- ✗
The system violates individual fairness
Why it's wrong here
Individual fairness compares similar individuals, not group error rates.
- ✗
The system suffers from selection bias
Why it's wrong here
Selection bias is a data source issue, not necessarily indicated by differing false positive rates.
- ✓
The system violates equalised odds
Why this is correct
Equalised odds demands equal false positive rates across groups; significant differences indicate unfairness.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
The system is not calibrated
Why it's wrong here
Calibration compares predicted probabilities to actual outcomes, not group error rates.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the distinction between demographic parity and equalised odds, trapping candidates who assume that equal selection rates (demographic parity) automatically guarantee fairness across all error types.
Trap categories for this question
Similar concept trap
Individual fairness compares similar individuals, not group error rates.
Detailed technical explanation
How to think about this question
Equalised odds is formally defined as requiring that the prediction Ŷ is conditionally independent of the protected attribute A given the true outcome Y, i.e., P(Ŷ=1 | A=a, Y=y) = P(Ŷ=1 | A=b, Y=y) for all groups a,b and outcomes y. This ensures both false positive and false negative rates are equal across groups. In practice, achieving equalised odds often requires post-processing adjustments or constrained optimisation during training, and it is a stronger condition than demographic parity (which only requires equal selection rates) or equal opportunity (which only requires equal true positive rates).
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.
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.
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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: The system violates equalised odds — Equalised odds requires that a model's false positive rates and true positive rates are equal across all demographic groups. Since the vendor's system has significantly different false positive rates across groups, it violates the equalised odds fairness criterion, even if demographic parity (equal selection rates) is satisfied. This is a core fairness metric in AI governance, as it ensures that errors are distributed equitably.
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
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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