AI0-001 AI Security, Ethics and Governance — 10 Questions

Question 1

A 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?

Accepted Answer

Apply data anonymization techniques to the training dataset.. Option B is correct because data anonymization ensures that patient identities are removed from training data, preventing re-identification of opt-out patients. Option A is incorrect because access control does not address data already in the model. Option C is incorrect because encryption protects data in transit/rest but does not prevent data leakage from model outputs. Option D is incorrect because differential privacy adds noise to queries but does not directly remove specific patient data from model results.

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Implement role-based access control (RBAC) on the AI model's inference API.

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Use differential privacy during model training.

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Encrypt the training data at rest and in transit.

Question 2

A financial institution is implementing an AI-based fraud detection system. The compliance officer is concerned about potential bias in the model that could lead to unfair treatment of certain customer groups. Which governance practice should be prioritized to address this concern?

Accepted Answer

Schedule regular bias audits using fairness metrics.. Regular bias audits using fairness metrics (Option B) are the correct governance practice because they provide a systematic, quantitative method to detect and measure disparate impact across protected groups. Unlike simply collecting more data, audits directly evaluate model outputs for statistical parity, equal opportunity, or other fairness definitions, enabling the institution to identify and remediate bias proactively. This aligns with regulatory expectations for ongoing monitoring and accountability in AI governance.

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Increase the diversity of the training data by collecting more samples from underrepresented groups.

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Retrain the model every month with the latest transaction data.

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Use SHAP values to provide explanations for each prediction.

Question 3

A company uses a machine learning model to recommend products to customers. The marketing team notices that the model is recommending high-profit items more frequently than low-profit items, even when customers are likely to prefer the latter. This behavior is causing customer dissatisfaction. Which approach would best align the model with customer preferences while maintaining profitability?

Accepted Answer

Use a multi-objective optimization framework to balance profit and customer satisfaction.. Option D is correct because multi-objective optimization allows the model to balance multiple goals (e.g., profit and customer satisfaction) explicitly. Option A is incorrect because it still prioritizes profit, which may not address satisfaction. Option B is incorrect because it completely removes profit, which may harm business goals. Option C is incorrect because it only adjusts profit weights without true multi-objective framework.

Answer

Train the model with a loss function that weights profit more heavily than customer satisfaction.

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Adjust the model's hyperparameters to reduce the influence of profit features.

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Remove profit data from the training set and only use customer preference data.

Question 4

An AI system used for resume screening is found to consistently rank male candidates higher than female candidates with similar qualifications. The HR director wants to remediate this bias without significantly reducing model accuracy. Which technique should be applied?

Accepted Answer

Apply adversarial debiasing to the model during training.. Adversarial debiasing is the correct technique because it directly addresses bias during training by introducing an adversarial network that attempts to predict the protected attribute (e.g., gender) from the model's predictions. The main model is trained to maximize accuracy while minimizing the adversary's ability to infer the protected attribute, thereby reducing bias without a significant drop in predictive performance. This approach is more effective than simple feature removal or data collection because it actively learns to remove correlations between the protected attribute and the output.

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Use a random selection of candidates to avoid bias.

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Remove the gender feature from the dataset and retrain.

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Collect more training data from underrepresented groups.

Question 5

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?

Accepted Answer

Implement a human-in-the-loop review process for high-risk responses.. Option C is correct because 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.

Answer

Use explainable AI techniques to understand why the chatbot generates certain responses.

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Encrypt all chatbot conversations at rest and in transit.

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Anonymize the training data used to train the chatbot.

Question 6

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?

Accepted Answer

Apply data augmentation techniques to simulate low-light conditions in the training dataset.. Option B is correct because 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.

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Replace the convolutional layers with transformer layers to improve attention.

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Use adversarial training to add imperceptible perturbations to training images.

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Increase the model's depth by adding more convolutional layers.

Question 7

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?

Accepted Answer

Transparency and explainability. 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.

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Privacy by design

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Accountability

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Beneficence

Question 8

Which TWO of the following are best practices for securing an AI model against adversarial attacks?

Accepted Answer

Adversarial training with perturbed examples.. Option B is correct because adversarial training explicitly augments the training dataset with perturbed examples (e.g., using FGSM or PGD attacks) to teach the model to recognize and resist malicious inputs. This method directly hardens the model against evasion attacks by improving its decision boundary robustness.

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Model pruning to reduce the number of parameters.

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Increasing model complexity to capture more patterns.

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Hyperparameter optimization using grid search.

Question 9

Which THREE of the following are key components of an AI governance framework?

Accepted Answer

Regular auditing and monitoring for compliance.. Regular auditing and monitoring for compliance (A) is a key component of an AI governance framework because it ensures that AI systems operate within legal, ethical, and organizational policies over time. Continuous monitoring detects drift, bias, or security violations, while audits provide evidence of adherence to standards such as ISO/IEC 42001 or internal governance rules. Without this, governance becomes a static policy with no enforcement or verification.

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Cloud-based deployment for scalability.

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Model accuracy thresholds for production deployment.

Question 10

Which TWO of the following are effective techniques to detect data poisoning attacks in a training dataset?

Accepted Answer

Performing cross-validation to check for inconsistent model performance.. Option A is correct because cross-validation can reveal data poisoning by exposing inconsistent model performance across folds. If a poisoned subset causes the model to perform well on certain folds but poorly on others, it indicates that the training data may have been tampered with, as the model's behavior becomes unstable due to maliciously injected samples.

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Normalizing features to zero mean and unit variance.

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Using ensemble methods like random forest for training.

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Applying PCA to reduce dimensionality.