20+ practice questions focused on AI Security, Ethics and Governance — 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 Security, Ethics and Governance PracticeA healthcare organization deploys an AI system to analyze medical images and detect anomalies. During a routine audit, the security team discovers that the AI model occasionally returns results that include data from patients who have opted out of data sharing. Which security control should be implemented to prevent this violation?
Explanation: Data anonymization techniques applied to the training dataset remove personally identifiable information (PII) and ensure that data from patients who opted out of data sharing cannot be reconstructed in model outputs. This directly prevents the violation of returning data from opt-out patients. Role-based access control (RBAC) on the inference API controls who can access the model but does not prevent the model from leaking sensitive data. Differential privacy adds noise to training or queries to protect individual contributions, but it does not guarantee removal of specific opt-out data; it may still allow leakage if the model memorizes. Encryption protects data in transit and at rest but does not affect model outputs. Therefore, option A is the most effective control for this specific violation.
A company is developing an AI chatbot for customer service. The legal team is concerned that the chatbot might generate responses that violate privacy regulations. Which governance mechanism should be implemented to mitigate this risk?
Explanation: A human-in-the-loop (HITL) review process directly addresses the risk of privacy violations by ensuring that high-risk responses are reviewed by a human before being sent to the customer. This governance mechanism provides a safety net for unpredictable outputs from the generative AI model, which may inadvertently leak personally identifiable information (PII) or violate data protection regulations like GDPR or CCPA. Unlike technical controls that only reduce the attack surface, HITL offers real-time compliance oversight for the chatbot's natural language generation (NLG) outputs.
A self-driving car company is testing an AI model for pedestrian detection. During simulation, the model fails to detect pedestrians in low-light conditions. The safety team wants to improve robustness without retraining the entire model from scratch. Which approach is most appropriate?
Explanation: Data augmentation techniques, such as adjusting brightness, contrast, and adding noise, can synthetically create low-light training examples from existing data. This improves the model's robustness to low-light conditions without requiring a full retraining from scratch, as it directly addresses the distribution shift in the input data.
An e-commerce company uses an AI system to set dynamic prices for products. A customer complains that the price they see is higher than the price shown to a friend for the same product at the same time. The company wants to ensure pricing fairness. Which ethical principle should guide the redesign of the pricing algorithm?
Explanation: Transparency and explainability is the correct principle because the core issue is that the customer cannot understand why the AI system set a different price for them compared to their friend. Redesigning the algorithm to provide clear, understandable reasons for price variations—such as demand, purchase history, or time of day—directly addresses this lack of visibility. This principle ensures that the system's decision-making process is open to scrutiny, which is essential for building trust and resolving fairness complaints in dynamic pricing models.
Which TWO of the following are effective techniques to detect data poisoning attacks in a training dataset?
Explanation: Option A is correct because cross-validation exposes inconsistencies in model performance: poisoned samples cause the model to fit corrupted patterns, so validation folds drawn from clean data show degraded or highly variable accuracy, precision, or recall compared with training folds, revealing the poisoning. Option E is correct because data poisoning often injects samples with anomalous feature values or label-feature mismatches, and statistical outlier detection on feature distributions (e.g., z-score, IQR, or Mahalanobis distance) can flag those suspicious records for review or removal. Option B is not a detection technique; normalization is a preprocessing step that scales features to zero mean and unit variance but does not identify poisoned samples. Option C is not a detection technique; random forest is a training algorithm that may be robust to some noise but does not itself detect poisoning. Option D is not a detection technique; PCA reduces dimensionality for feature extraction or visualization and does not flag malicious data points.
+15 more AI Security, Ethics and Governance questions available
Practice all AI Security, Ethics and Governance questions1. Baseline your knowledge
Start with 10 questions to gauge your current understanding of AI Security, Ethics and Governance. 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 Security, Ethics and Governance 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 Security, Ethics and Governance is tested as part of the CompTIA AI+ AI0-001 blueprint. Practicing with targeted AI Security, Ethics and Governance questions ensures you can handle any format or difficulty that appears.
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