AI0-001 AI Security, Ethics and Governance Practice Question
A financial institution is implementing an AI-based fraud detection system. The compliance officer is concerned about potential bias in the model that could lead to unfair treatment of certain customer groups. Which governance practice should be prioritized to address this concern?
⚠ Common exam trap
CompTIA often tests the distinction between interpretability (explaining a single prediction) and fairness (systematic bias across groups), leading candidates to mistakenly choose SHAP values (Option D) as a bias mitigation technique when it is only an explanation tool.
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
✓
Schedule regular bias audits using fairness metrics.
Regular bias audits using fairness metrics (Option B) are the correct governance practice because they provide a systematic, quantitative method to detect and measure disparate impact across protected groups. Unlike simply collecting more data, audits directly evaluate model outputs for statistical parity, equal opportunity, or other fairness definitions, enabling the institution to identify and remediate bias proactively. This aligns with regulatory expectations for ongoing monitoring and accountability in AI governance.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the diversity of the training data by collecting more samples from underrepresented groups.
Why it's wrong here
While diversity is important, simply adding data without analysis may not resolve bias and could introduce new issues.
- ✓
Schedule regular bias audits using fairness metrics.
Why this is correct
Bias audits with metrics like demographic parity can detect unfair treatment and guide mitigation.
- ✗
Retrain the model every month with the latest transaction data.
Why it's wrong here
Retraining may not address bias; it could even perpetuate existing biases if new data is similarly biased.
- ✗
Use SHAP values to provide explanations for each prediction.
Why it's wrong here
Explainability helps understand model decisions but does not directly identify or correct bias.
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.
About these practice questions
This AI0-001 question is part of Courseiva's 754-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
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.