- A
Analyze the model for disparate impact using statistical tests
Disparate impact analysis helps identify whether the model adversely affects a protected group.
- B
Review features like zip code for potential proxy discrimination
Zip codes can act as proxies for race or income, and reviewing them helps identify sources of bias.
- C
Remove all features that could be correlated with protected attributes
Why wrong: Removing correlated features may reduce predictive performance and does not guarantee fairness due to proxies.
- D
Implement a fairness metric like demographic parity or equalized odds
Why wrong: Implementing a metric is important, but the question asks for actions to mitigate bias; metric implementation alone does not modify the model or data.
- E
Apply differential privacy to the training data
Why wrong: Differential privacy protects individual privacy but does not mitigate bias across groups.
AI0-001 AI Governance and Ethics Practice Question
This AI0-001 practice question tests your understanding of ai governance and ethics. 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 wants to ensure its credit scoring model is fair across demographic groups. The model currently uses features like zip code, income, and credit history. To mitigate potential bias, which TWO actions should the data science team prioritize?
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
Analyze the model for disparate impact using statistical tests
Option A is correct because analyzing the model for disparate impact using statistical tests (e.g., the 80% rule or chi-square test) directly measures whether the model produces systematically different outcomes for protected groups. This is a foundational step in fairness auditing, as it quantifies bias before any mitigation is applied, aligning with regulatory expectations like the Equal Credit Opportunity Act (ECOA).
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.
- ✓
Analyze the model for disparate impact using statistical tests
Why this is correct
Disparate impact analysis helps identify whether the model adversely affects a protected group.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Review features like zip code for potential proxy discrimination
Why this is correct
Zip codes can act as proxies for race or income, and reviewing them helps identify sources of bias.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Remove all features that could be correlated with protected attributes
Why it's wrong here
Removing correlated features may reduce predictive performance and does not guarantee fairness due to proxies.
- ✗
Implement a fairness metric like demographic parity or equalized odds
Why it's wrong here
Implementing a metric is important, but the question asks for actions to mitigate bias; metric implementation alone does not modify the model or data.
- ✗
Apply differential privacy to the training data
Why it's wrong here
Differential privacy protects individual privacy but does not mitigate bias across groups.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the distinction between detection and mitigation steps, so candidates mistakenly pick a mitigation action (like D) or a privacy technique (like E) as a first priority, overlooking that bias must first be measured and understood.
Detailed technical explanation
How to think about this question
Disparate impact analysis often uses the 'four-fifths rule' from the US EEOC, where a selection rate for a protected group less than 80% of the highest group's rate indicates potential bias. Statistical tests like the chi-square test for independence or Fisher's exact test can formally assess whether observed differences are statistically significant, accounting for sample size. In practice, zip code can act as a proxy for race due to historical redlining, so reviewing such features for proxy discrimination is critical before any modeling decisions.
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.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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FAQ
Questions learners often ask
What does this AI0-001 question test?
AI Governance and Ethics — This question tests AI Governance and Ethics — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Analyze the model for disparate impact using statistical tests — Option A is correct because analyzing the model for disparate impact using statistical tests (e.g., the 80% rule or chi-square test) directly measures whether the model produces systematically different outcomes for protected groups. This is a foundational step in fairness auditing, as it quantifies bias before any mitigation is applied, aligning with regulatory expectations like the Equal Credit Opportunity Act (ECOA).
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.
About these practice questions
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 →
Last reviewed: Jul 4, 2026
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.
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