AI0-001 AI Governance and Ethics Practice Question
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?
⚠ Common exam trap
CompTIA AI+ emphasizes that bias detection (statistical tests, proxy review) must come before mitigation. Many candidates incorrectly select mitigation actions like demographic parity or data removal as a first step, but the exam stresses that bias must first be measured and understood.
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
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).
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.
- ✓
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.
- ✗
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.
About these practice questions
This AI0-001 question is part of Courseiva's 962-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.