AI0-001 AI Security, Ethics and Governance Practice Test 2 — 15 Questions

Question 1

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?

Choose an answer to begin — your selection is scored in the full session.

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 2

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 3

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.. 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.

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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 4

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.. 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 5

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 6

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

Accepted Answer

Adversarial training with perturbed examples.. 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 7

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 8

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.. 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.

Question 9

A healthcare organization is deploying an AI system to analyze patient records and recommend treatment plans. To comply with data privacy regulations, what is the most important security measure to implement?

Accepted Answer

Anonymize patient data before processing. Anonymizing patient data before processing is the most important security measure because it directly addresses data privacy regulations like HIPAA and GDPR by removing personally identifiable information (PII) from the dataset. This ensures that even if a breach occurs, the data cannot be linked back to an individual, thereby minimizing compliance risk. While other measures like encryption and access control are essential, anonymization is the foundational step for lawful AI processing of sensitive health data.

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Enable detailed audit logging

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Encrypt all data at rest and in transit

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Implement role-based access control

Question 10

A financial institution uses an AI model to approve loan applications. The model was trained on historical data that included biased lending practices. The bank's ethics committee wants to mitigate bias without removing protected attributes. Which approach best balances fairness and model performance?

Accepted Answer

Apply adversarial debiasing during training. Adversarial debiasing is the best approach because it directly optimizes the model to reduce bias during training while preserving predictive accuracy. It uses an adversarial network that tries to predict the protected attribute from the model's predictions, forcing the main model to learn representations that are less correlated with that attribute. This allows the bank to keep protected attributes in the data (as required by the ethics committee) while actively mitigating bias.

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Retrain the model using a balanced dataset

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Remove all protected attributes from the training data

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Post-process model outputs to adjust for demographic parity

Question 11

A company is developing an AI chatbot for customer service. They want to ensure the bot does not generate offensive or harmful responses. Which governance practice should be implemented first?

Accepted Answer

Implement a content filter to screen responses before delivery. A content filter acts as a real-time safety gate that screens every response generated by the AI model before it reaches the customer. This is the first line of defense against offensive or harmful outputs, as it can catch toxic language, PII leaks, or policy violations immediately, even if the underlying model has not been fully sanitized. Without such a filter, harmful responses could be delivered before any other governance measure (like human review or policy creation) can intervene.

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Set up a human-in-the-loop review process

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Create a usage policy for acceptable bot behavior

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Sanitize training data to remove toxic examples

Question 12

An AI system used for autonomous driving is found to have a lower accuracy in detecting pedestrians with darker skin tones. The development team wants to address this ethical issue. Which action is most effective?

Accepted Answer

Augment the training dataset with more images of pedestrians with darker skin. Augmenting the training dataset with more images of pedestrians with darker skin directly addresses the root cause of the bias: underrepresentation in the training data. By providing a more balanced and diverse dataset, the model can learn more robust features for all skin tones, reducing accuracy disparity without altering the algorithm's core logic or introducing arbitrary thresholds.

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Conduct additional testing to measure the disparity

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Replace the object detection algorithm with a different one

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Adjust the model's decision threshold for pedestrian detection

Question 13

Which TWO practices are most effective for ensuring the security of an AI model against adversarial attacks?

Accepted Answer

Input sanitization and validation. Input sanitization and validation (C) is correct because it prevents adversarial inputs—such as specially crafted perturbations or injection strings—from reaching the model's inference pipeline. By filtering, encoding, or rejecting malicious data at the application layer, the model's decision boundary is protected from manipulation. This is a fundamental defense-in-depth measure against evasion and poisoning attacks.

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Encrypting the model weights

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Continuous model monitoring

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Rate limiting API access

Question 14

You are a security engineer at a large e-commerce company that uses an AI-based recommendation system. The system is deployed on a Kubernetes cluster and uses a TensorFlow model served via REST API. Recently, the security team detected unusual API calls that caused the model to return incorrect recommendations. Analysis shows that the inputs were crafted to maximize prediction error. The team suspects an adversarial attack. You need to implement a solution that detects and mitigates such attacks in real-time without requiring model retraining. Which approach should you take?

Accepted Answer

Implement an input validation filter to detect and block anomalous inputs. An input validation filter can detect and block adversarial inputs in real-time by analyzing statistical properties (e.g., outlier detection, perturbation magnitude) without modifying the model. This approach is lightweight, operates at the API gateway level, and does not require retraining, making it suitable for immediate deployment against crafted inputs that maximize prediction error.

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Increase the number of model replicas to distribute the load

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Retrain the model with adversarial examples

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Roll back the model to a previous version that was not attacked

Question 15

You are an AI governance officer at a bank that uses a machine learning model to predict credit risk. The model was developed by an external vendor and uses a proprietary algorithm. The bank's compliance team has determined that the model must be explainable to meet regulatory requirements. However, the vendor claims the model is a 'black box' and cannot provide explanations. You need to ensure compliance while maintaining the model's performance. What is the best course of action?

Accepted Answer

Use a model-agnostic explanation technique like SHAP. D is correct because model-agnostic explanation techniques like SHAP (SHapley Additive exPlanations) can provide post-hoc interpretability for any black-box model without requiring access to its internal structure or proprietary algorithm. This allows the bank to meet regulatory explainability requirements while preserving the vendor's proprietary model and its predictive performance.

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Ignore the requirement as the model is proprietary

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Ask the vendor to develop a custom explanation module

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Replace the model with a simpler, interpretable model