AI0-001 AI Security, Ethics and Governance — Timed 25 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?

25 minute time limit — choose an answer to begin.

Accepted Answer

Apply data anonymization techniques to the training dataset.. 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.

Answer

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

Answer

Use differential privacy during model training.

Answer

Encrypt the training data at rest and in transit.

Question 2

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.. A multi-objective optimization framework explicitly allows the model to balance multiple goals, such as profit and customer satisfaction, by optimizing both objectives simultaneously. Option A is incorrect because weighting profit more heavily would exacerbate the issue and further ignore customer preferences. Option C is incorrect because adjusting hyperparameters to reduce profit feature influence is not a principled way to balance objectives and may not effectively improve satisfaction. Option D is incorrect because removing profit data entirely ignores legitimate business goals, potentially harming profitability.

Answer

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

Answer

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

Answer

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

Question 3

A security analyst reviews the log file from an AI model server. What is the most likely cause of the crash?

Accepted Answer

A malicious input triggered a buffer overflow. A malicious input triggering a buffer overflow is the most likely cause of the crash because AI model servers often process user-supplied data in native code (e.g., C/C++ extensions or TensorFlow ops) that lack bounds checking. An attacker can craft an input that overflows a fixed-size buffer, corrupting memory and causing a segmentation fault or denial of service. This is a well-known AI security vulnerability, distinct from generic resource exhaustion or data-type errors.

Answer

The server ran out of memory due to high traffic

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The model weights were corrupted during loading

Answer

The model encountered an unknown data type

Question 4

An organization implements AI governance following the NIST AI Risk Management Framework. They need to ensure that all model decisions are logged with sufficient detail for later audit. Which logging requirement is most critical for traceability?

Accepted Answer

Timestamp, input data, output, and model version. Timestamp, input data, output, and model version together provide full traceability for audit. Option A is wrong because logging only input data and model name misses outputs, timestamp, and version, which are essential for traceability. Option B is wrong because source code and training dataset hash are not part of the inference audit trail; they are more relevant to model development. Option C is wrong because logging only model outputs and confidence scores misses inputs and model version, making it impossible to fully trace decisions.

Answer

Input data and model name only

Answer

Source code and training dataset hash

Answer

Model outputs and confidence scores only

Question 5

A data scientist trains a sentiment analysis model on user reviews. To ensure transparency, they want to explain why the model classified a particular review as negative. Which explainability technique should they use?

Accepted Answer

SHAP (SHapley Additive exPlanations). SHAP (SHapley Additive exPlanations) provides per-feature attribution for individual predictions, making it suitable for explaining why a particular review was classified as negative. Option A is incorrect because a decision tree surrogate model is a global explanation method, not a local explanation for a single instance. Option B is incorrect because PCA is a dimensionality reduction technique, not an explainability method. Option D is incorrect because t-SNE is used for high-dimensional data visualization, not for explaining model predictions.

Answer

Decision tree surrogate model

Answer

Principal component analysis

Answer

t-SNE dimensionality reduction

Question 6

A multinational corporation deploys an AI recruitment tool that must comply with GDPR's right to explanation. Which practice best ensures the tool meets this requirement?

Accepted Answer

Implement a system to output the key factors influencing each decision. GDPR's right to explanation (Article 22) requires that data subjects receive meaningful information about the logic involved in automated decisions. Implementing a system that outputs the key factors influencing each decision directly satisfies this requirement. Option A is wrong because explicit consent pertains to data processing permissions, not to post-decision explanations. Option C is wrong because anonymization addresses data privacy, not the transparency of decision logic. Option D is wrong while human review adds a layer of oversight, it does not inherently provide an explanation of the decision factors.

Answer

Obtain explicit consent from candidates

Answer

Anonymize all candidate data before processing

Answer

Always have a human review the AI's recommendations

Question 7

Refer to the exhibit. A security engineer is reviewing an AI access control policy. Which of the following is the most significant security weakness in this policy?

Accepted Answer

The policy does not limit the number of inference requests. The policy lacks rate limiting on inference requests, which is the most significant security weakness as it enables model extraction attacks. Option A (wide IP range) is not the most significant because the policy still restricts to corporate ranges. Option B is incorrect because MFA is already required. Option C is a minor concern compared to the threat of model theft via unlimited requests.

Answer

The policy allows access from a wide private IP range

Answer

The policy does not require multi-factor authentication

Answer

The policy grants 'audit_log' access to data scientists

Question 8

A healthcare AI system misdiagnosed patients due to adversarial inputs. What security measure should be prioritized?

Accepted Answer

Implement adversarial training. (Implement adversarial training) is correct because adversarial training makes the model robust to input manipulation. Option A (Encrypt all patient data) protects data privacy but not model integrity. Option B (Use stronger authentication) is for access control. Option C (Regular software updates) is general maintenance and does not specifically address adversarial inputs.

Answer

Encrypt all patient data

Answer

Use stronger authentication

Answer

Regular software updates

Question 9

A social media company's AI recommendation system pushes extreme content to users, causing harm. Which ethical principle is most violated?

Accepted Answer

Non-maleficence. Non-maleficence (do no harm) is the principle most directly violated because the AI system actively causes harm by pushing extreme content that damages users' mental health or incites harmful behavior. Unlike beneficence (doing good), non-maleficence focuses on avoiding harm, and the system's design fails to prevent foreseeable negative outcomes.

Answer

Autonomy

Answer

Justice

Answer

Beneficence

Question 10

Refer to the exhibit. Which assessment is most critical for ethical deployment?

Accepted Answer

Bias mitigation is needed. The exhibit (not shown) likely presents a confusion matrix or performance metrics indicating disparate impact across demographic groups. Option C is correct because bias mitigation is the most critical ethical assessment when model outcomes systematically disadvantage protected groups, violating fairness principles and potentially breaching regulations like GDPR or AI Act. Without addressing bias, the model cannot be ethically deployed regardless of other improvements.

Answer

Feature engineering should be improved

Answer

Data collection needs expansion

Answer

Model retraining is required due to low recall

Question 11

A financial institution uses an AI model to approve small business loans. The model has a high approval rate for women-owned businesses but low for minority-owned businesses. The compliance officer is concerned about disparate impact. Which governance process should be implemented first?

Accepted Answer

Conduct a bias audit and fairness assessment using relevant metrics. A bias audit and fairness assessment should be conducted first to quantify the disparate impact and identify root causes. Option A is wrong because simply removing sensitive features does not guarantee fairness and may be insufficient or illegal. Option C is wrong because publishing decision-making criteria without first addressing bias could expose the institution to liability and undermine trust. Option D is wrong because adjusting the approval threshold without thorough analysis can be arbitrary, may not address underlying bias, and could lead to reverse discrimination or mask systemic issues.

Answer

Remove gender and ethnicity features from the model

Answer

Publish the model's decision-making criteria to the public

Answer

Immediately adjust the approval threshold to equalize rates

Question 12

A social media company uses an AI content moderation system to filter hate speech. The system uses a natural language processing model trained on user reports. Recently, the model's false positive rate has increased, blocking legitimate posts. An internal audit reveals that a coordinated group of users has been falsely reporting harmless posts, causing the model to learn incorrect patterns. The company needs to address the attack and restore accuracy. The engineering team can modify the training pipeline. What is the most effective first step?

Accepted Answer

Redesign the training pipeline to incorporate a reputation system for reporting users. The root cause is a coordinated attack where malicious users exploit the reporting mechanism to poison the training data. Incorporating a reputation system into the training pipeline allows the model to weigh or filter user reports based on the trustworthiness of the reporting user, directly mitigating the impact of false reports without discarding legitimate feedback. This addresses the adversarial behavior at the source, restoring accuracy by ensuring the model learns from reliable signals.

Answer

Increase the weight of non-reported posts to counteract the reported posts' influence

Answer

Apply adversarial training to make the model robust to crafted inputs

Answer

Retrain the model on a dataset that excludes all user-reported posts

Question 13

A manufacturing company uses a predictive maintenance AI system to schedule equipment repairs. The system was trained on sensor data from machinery. Recently, the system has been missing failures, leading to unexpected downtime. An investigation reveals that the sensor data from one plant has been corrupted due to a sensor malfunction. The corrupted data was used in retraining. The company needs to restore system accuracy quickly. The data science team can access the training logs. What is the best course of action?

Accepted Answer

Roll back to the previous model version before the corrupt data was ingested, then clean the sensor data and retrain. Rolling back to the previous model version isolates the system from the corrupted sensor data that caused accuracy degradation. Cleaning the sensor data before retraining ensures the model learns from accurate patterns, restoring predictive maintenance reliability. This approach directly addresses the root cause—data corruption—without introducing new risks.

Answer

Switch to a simpler linear regression model that is less sensitive to data quality issues

Answer

Retrain the model using all available data, including the corrupted sensor data

Answer

Apply a weight to sensor data from that plant to reduce its influence

Question 14

Which TWO of the following are essential components of a responsible AI governance framework?

Accepted Answer

Assignment of a responsible owner for each AI system's outcomes. Assigning a responsible owner for each AI system's outcomes ensures accountability, which is a core principle of AI governance. This owner is typically a designated individual or team that oversees the system's lifecycle, including monitoring for bias, compliance with regulations, and handling incidents. Without clear ownership, there is no single point of contact for ethical or legal issues, making governance ineffective.

Answer

Using ensemble methods to reduce overfitting

Answer

Automated hyperparameter tuning to improve accuracy

Answer

Deploying models on dedicated hardware to reduce latency

Question 15

An organization implements the above access control policy for its AI model registry. During an audit, the auditor discovers that a data scientist deployed a model to production without authorization. Which of the following is the most likely cause?

Accepted Answer

The policy allows data scientists to deploy to staging, but they exploited a gap in enforcement to promote to production without the required approval. The scenario describes a data scientist who bypassed the required approval step by exploiting a gap in the policy enforcement mechanism. The policy explicitly requires approval for production deployments, but the data scientist was able to promote a model from staging to production without that approval, indicating a flaw in the access control implementation or the separation of duties between staging and production environments.

Answer

The ML engineer role has approval required, but the manager approved the deployment

Answer

The time window condition blocked the deployment, but it was overridden by an administrator

Answer

The auditor lacks 'deploy_to_production' permission, so they missed the deployment

Question 16

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.

Answer

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

Answer

Retrain the model every month with the latest transaction data.

Answer

Use SHAP values to provide explanations for each prediction.

Question 17

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.

Answer

Use a random selection of candidates to avoid bias.

Answer

Remove the gender feature from the dataset and retrain.

Answer

Collect more training data from underrepresented groups.

Question 18

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.

Answer

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

Answer

Encrypt all chatbot conversations at rest and in transit.

Answer

Anonymize the training data used to train the chatbot.

Question 19

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.

Answer

Replace the convolutional layers with transformer layers to improve attention.

Answer

Use adversarial training to add imperceptible perturbations to training images.

Answer

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

Question 20

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.

Answer

Privacy by design

Answer

Accountability

Answer

Beneficence