AI0-001 AI Concepts and Foundations Practice Question
Exhibit
{
"fairness_metric": "demographic_parity",
"threshold": 0.1,
"protected_attributes": ["race", "gender"]
}Refer to the exhibit. An AI auditor reviews the fairness configuration. What is the purpose of this policy?
⚠ Common exam trap
CompTIA often tests the distinction between demographic parity (equal positive prediction rates) and equalized odds (equal error rates), so candidates mistakenly choose 'equal error rates' when they see a fairness policy that actually enforces demographic parity.
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
✓
Ensure equal positive prediction rates across groups
The policy sets a fairness constraint that requires the model's positive prediction rate (the fraction of instances predicted as the positive class) to be equal across all defined groups. This is a standard demographic parity requirement, which is implemented by adjusting the decision threshold or reweighting training data to ensure that each group receives the same proportion of positive predictions, regardless of the actual outcome distribution.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Ensure equal error rates across groups
Why it's wrong here
Equal error rates is a genuine fairness objective, but the exhibit's policy targets the specific fairness metric shown, not error-rate parity across groups. It is tempting because equalised odds and equal opportunity are standard fairness definitions, and would be correct if the configuration measured false positive or false negative rates per group.
- ✓
Ensure equal positive prediction rates across groups
Why this is correct
Equal positive prediction rates across groups is the definition of demographic parity, so the policy constrains the model to produce the same proportion of favourable outcomes for each protected group, regardless of differing base rates or accuracy.
- ✗
Ensure equal accuracy across groups
Why it's wrong here
Equal accuracy across groups measures overall correctness, but fairness policies typically equalise error rates or outcomes rather than raw accuracy, which can mask unequal false positive and false negative distributions. It is tempting because accuracy parity sounds like fairness, and would be right if the policy explicitly targeted predictive performance parity.
- ✗
Ensure model interpretability
Why it's wrong here
Interpretability explains how a model reaches decisions, whereas a fairness configuration targets disparate impact across protected groups. It is tempting because interpretability tooling is often deployed alongside fairness dashboards, and would be correct if the policy's goal were explaining individual predictions rather than measuring group outcomes.
Quick reference
RAID Level Comparison
| RAID Level | Min Disks | Fault Tolerance | Read | Write | Usable Capacity |
|---|---|---|---|---|---|
| RAID 0 | 2 | None | Excellent | Excellent | 100% |
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| RAID 10 | 4 | 1 disk per mirror | Excellent | Good | 50% |
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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.