20+ practice questions focused on AI Governance and Ethics — one of the most tested topics on the CompTIA AI+ AI0-001 exam. Each question includes a detailed explanation so you learn why the right answer is correct.
Start AI Governance and Ethics PracticeA healthcare AI system uses patient data to predict disease risk. To comply with HIPAA and reduce the risk of re-identification, which technique should be applied to the training data before model development?
Explanation: Differential privacy (Option C) is the correct technique because it adds calibrated noise to the training data or model outputs, providing a mathematical guarantee against re-identification even if an attacker has auxiliary information. This directly addresses HIPAA's requirement to protect patient privacy while preserving statistical utility for disease risk prediction.
An AI team is developing a model that approves loan applications. The dataset contains historical loan decisions where a protected group was disproportionately denied loans. The team wants to ensure the model does not perpetuate this bias. Which fairness metric should be used during validation to directly measure whether the model's positive prediction rate is equal across groups?
Explanation: Demographic parity requires the probability of a positive prediction (loan approval) to be equal across groups. This directly addresses the concern of perpetuating historical denial rates. Equalised odds measures error rates, not positive prediction rates.
A company is deploying an AI system that screens job applications. According to the EU AI Act, this system is likely classified as high-risk because it affects employment opportunities. Which requirement must the company implement for high-risk AI systems?
Explanation: The EU AI Act requires human oversight for high-risk AI systems to allow operators to override or stop the system's decisions when necessary. The other options are not mandated by the Act for high-risk systems.
A data scientist is using SHAP to explain a complex ensemble model's predictions. A business stakeholder asks why a particular prediction was made. The data scientist wants to show the most influential features for that single prediction. Which SHAP visualisation is most appropriate?
Explanation: A SHAP force plot is specifically designed to visualize the contribution of each feature to a single prediction, showing how features push the prediction from the base value (average model output) to the final prediction. This makes it the ideal choice for explaining an individual prediction to a business stakeholder, as it provides a clear, localized explanation of feature impacts.
A financial institution needs to deploy a credit scoring model that is interpretable to regulators. The model must provide clear reasons for each decision. Which model type should the institution choose?
Explanation: Glass-box models like logistic regression or decision trees are inherently interpretable and can provide clear, auditable reasons for each prediction. Black-box models require post-hoc explainability methods, which may not be sufficient for regulatory scrutiny.
+15 more AI Governance and Ethics questions available
Practice all AI Governance and Ethics questions1. Baseline your knowledge
Start with 10 questions to gauge your current understanding of AI Governance and Ethics. This tells you whether you need a concept refresher or just practice.
2. Review every explanation
For each question — right or wrong — read the full explanation. Understanding why an answer is correct is more valuable than knowing the answer itself.
3. Focus on exam traps
AI Governance and Ethics questions on the AI0-001 frequently use trap wording. Look for subtle differences in answers that test your precision, not just general knowledge.
4. Reach 80% consistently
Do repeated sessions until you score 80%+ three times in a row. Then move to mixed-mode practice to test cross-topic recall under realistic conditions.
The exact number varies per candidate. AI Governance and Ethics is tested as part of the CompTIA AI+ AI0-001 blueprint. Practicing with targeted AI Governance and Ethics questions ensures you can handle any format or difficulty that appears.
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Difficulty is subjective, but AI Governance and Ethics is a high-priority exam concept tested in multiple ways — direct recall, scenario analysis, and command-output interpretation. Consistent practice is the best way to build confidence.
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