- A
Remove the ZIP code feature from the model
Why wrong: Removing ZIP code alone may not eliminate bias if other features correlate; also could reduce model accuracy.
- B
Increase the decision threshold for those ZIP codes
Why wrong: Adjusting threshold per ZIP code may be seen as discriminatory and doesn't address root cause.
- C
Discontinue use of the model for those ZIP codes
Why wrong: Dropping the model for some areas is not a solution and may lead to inconsistent practices.
- D
Retrain the model with fairness constraints
Fairness constraints can reduce bias while maintaining overall performance, a more comprehensive solution.
Question 30 of 1,000
AI0-001 AI Security, Ethics and Governance Practice Question
This AI0-001 practice question tests your understanding of ai security, ethics and governance. 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.
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?
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
Option D is correct because 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.
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.
- ✗
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.
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 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.
Detailed technical explanation
How to think about this question
Fairness constraints like equalized odds require that the model's false positive and false negative rates are equal across protected groups, which is enforced by adding a regularization term to the loss function during training. In practice, this can be implemented using adversarial debiasing or reweighting training samples to reduce the influence of biased features. A real-world scenario is the use of 'fairness through unawareness' being insufficient, as demonstrated by the US Housing and Urban Development (HUD) cases where proxy features (e.g., ZIP code) still led to redlining even when the feature itself was removed.
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
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FAQ
Questions learners often ask
What does this AI0-001 question test?
AI Security, Ethics and Governance — This question tests AI Security, Ethics and Governance — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Retrain the model with fairness constraints — Option D is correct because 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.
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.
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Last reviewed: Jul 4, 2026
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