AI0-001 AI Security, Ethics and Governance Practice Question
A financial institution uses an AI model to approve loans. The model uses features including credit score and ZIP code. During an audit, it is discovered that the model has a high false positive rate for loan default predictions in certain ZIP codes. What should the institution do to address this?
⚠ Common exam trap
The AI0-001 exam often tests the misconception that removing a sensitive feature (like ZIP code) is sufficient to eliminate bias, when in reality correlated proxy features can perpetuate discrimination—a concept known as 'fairness through unawareness' being a flawed approach.
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 fairness constraints
Retraining the model with fairness constraints directly addresses the root cause of the bias—the model's learned correlations between ZIP code and default risk. Fairness constraints, such as demographic parity or equalized odds, are applied during training to ensure the model's predictions are not systematically skewed against certain groups. This approach preserves the predictive power of legitimate features while mitigating discriminatory outcomes, aligning with AI governance principles.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Remove the ZIP code feature from the model
Why it's wrong here
Removing ZIP code alone may not eliminate bias if other features correlate; also could reduce model accuracy.
- ✗
Increase the decision threshold for those ZIP codes
Why it's wrong here
Adjusting threshold per ZIP code may be seen as discriminatory and doesn't address root cause.
- ✗
Discontinue use of the model for those ZIP codes
Why it's wrong here
Dropping the model for some areas is not a solution and may lead to inconsistent practices.
- ✓
Retrain the model with fairness constraints
Why this is correct
Fairness constraints can reduce bias while maintaining overall performance, a more comprehensive solution.
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.