- A
Accountability
Why wrong: Accountability is about who is responsible, but the primary violation is bias.
- B
Fairness
Fairness requires non-discrimination, which is violated here.
- C
Transparency
Why wrong: Transparency is about explainability, not directly addressing discrimination.
- D
Privacy
Why wrong: Privacy relates to data protection, not bias.
Question 147 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 bank uses an AI model to approve loans. During an audit, it is found that the model denies loans at a higher rate for a certain ethnic group. Which governance principle is primarily violated?
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
✓
Fairness
The model's disparate impact on a specific ethnic group directly violates the principle of Fairness, which requires that AI systems do not discriminate based on protected attributes such as race, ethnicity, or gender. In lending, fairness is often assessed using metrics like demographic parity or equal opportunity, and a higher denial rate for one group indicates a lack of algorithmic fairness.
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.
- ✗
Accountability
Why it's wrong here
Accountability is about who is responsible, but the primary violation is bias.
- ✓
Fairness
Why this is correct
Fairness requires non-discrimination, which is violated here.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Transparency
Why it's wrong here
Transparency is about explainability, not directly addressing discrimination.
- ✗
Privacy
Why it's wrong here
Privacy relates to data protection, not bias.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the distinction between Fairness and Transparency, where candidates mistakenly choose Transparency because they think 'explaining the bias' is the primary issue, but the question asks which principle is violated by the biased outcome itself.
Detailed technical explanation
How to think about this question
Under the hood, fairness violations often stem from biased training data or proxy features (e.g., zip code correlating with ethnicity) that cause the model to learn spurious correlations. Real-world audits use disparate impact analysis (e.g., the 80% rule from US employment law) to quantify whether a protected group's approval rate is less than 80% of the most favored group's rate, triggering regulatory scrutiny. Even if the model is accurate overall, fairness constraints like equalized odds must be enforced during training or post-processing to mitigate such bias.
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
Got this wrong? Here's your next step.
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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: Fairness — The model's disparate impact on a specific ethnic group directly violates the principle of Fairness, which requires that AI systems do not discriminate based on protected attributes such as race, ethnicity, or gender. In lending, fairness is often assessed using metrics like demographic parity or equal opportunity, and a higher denial rate for one group indicates a lack of algorithmic fairness.
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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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 →
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Last reviewed: Jul 4, 2026
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