- A
Equalised odds
Why wrong: Equalized odds requires equal TPR and FPR across groups, not calibration.
- B
Demographic parity
Why wrong: Demographic parity would require equal positive prediction rates, not calibration.
- C
Individual fairness
Why wrong: Individual fairness is about similar individuals receiving similar predictions, not calibration.
- D
Calibration
Calibration checks that for a given predicted probability, the actual outcome rate is the same across groups.
Question 654 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.
An AI model for skin cancer detection achieves high accuracy but performs poorly on dark skin tones. The team wants to evaluate whether the model is calibrated across skin tones. Which fairness metric should they use?
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
✓
Calibration
Calibration is the correct metric because it directly measures whether the predicted probabilities of skin cancer match the actual outcomes across different skin tones. A model can have high overall accuracy but be miscalibrated for a subgroup if its confidence scores are systematically over- or under-confident for that group. In this scenario, the team needs to check if the model's risk scores are equally reliable for dark skin tones as for light skin tones, which is exactly what calibration assesses.
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.
- ✗
Equalised odds
Why it's wrong here
Equalized odds requires equal TPR and FPR across groups, not calibration.
- ✗
Demographic parity
Why it's wrong here
Demographic parity would require equal positive prediction rates, not calibration.
- ✗
Individual fairness
Why it's wrong here
Individual fairness is about similar individuals receiving similar predictions, not calibration.
- ✓
Calibration
Why this is correct
Calibration checks that for a given predicted probability, the actual outcome rate is the same across groups.
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 distinction between fairness metrics by presenting a scenario where 'accuracy' is high but subgroup performance differs, and candidates mistakenly choose equalized odds or demographic parity instead of recognizing that the core issue is confidence score reliability, i.e., calibration.
Trap categories for this question
Similar concept trap
Individual fairness is about similar individuals receiving similar predictions, not calibration.
Detailed technical explanation
How to think about this question
Calibration is typically evaluated using reliability diagrams or the expected calibration error (ECE), which bins predictions by confidence and compares the average predicted probability to the actual fraction of positives in each bin. For skin cancer detection, a miscalibrated model might assign a 90% confidence to a lesion on dark skin that actually has only a 60% chance of being malignant, leading to overconfident false negatives. Real-world deployment of such a model could cause delayed diagnosis for patients with darker skin, exacerbating healthcare disparities.
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: Calibration — Calibration is the correct metric because it directly measures whether the predicted probabilities of skin cancer match the actual outcomes across different skin tones. A model can have high overall accuracy but be miscalibrated for a subgroup if its confidence scores are systematically over- or under-confident for that group. In this scenario, the team needs to check if the model's risk scores are equally reliable for dark skin tones as for light skin tones, which is exactly what calibration assesses.
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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