AI0-001 AI Security, Ethics and Governance Practice Test 3 — 15 Questions

Question 1

Refer to the exhibit. A security analyst reviews the monitoring log for an AI fraud detection model. Which of the following is the most likely cause of the multiple alerts?

Accepted Answer

An adversarial attack attempt. Option B is correct because the combination of drift, high query rate, and degraded performance suggests an ongoing adversarial attack aimed at probing the model. Option A is wrong because data poisoning would have been detected earlier, not cause real-time drift. Option C is wrong because model retraining could cause temporary instability but not the specific query pattern. Option D is wrong because network issues would cause latency but not feature drift or query rate anomalies.

Answer

Data poisoning of the training dataset

Answer

A network hardware failure

Answer

A scheduled model retraining process

Question 2

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. Option D is correct because no restrictions are placed on the number of inference requests, allowing potential model extraction attacks. Option A is wrong because auditors need audit_log access for compliance. Option B is wrong because the policy correctly restricts to corporate IP ranges. Option C is wrong because MFA is required, which is a strong control.

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 3

Refer to the exhibit. A system administrator sees these logs from an AI inference pipeline. What is the most likely sequence of events?

Accepted Answer

An adversarial input was blocked by the security filter. Option A is correct because the adversarial input triggered the security filter, which then blocked the request. Option B is wrong because the input caused NaN, not filter failure. Option C is wrong because there is no evidence of data poisoning. Option D is wrong because the filter blocked it; it did not fail.

Answer

Data poisoning corrupted the model, causing NaN outputs

Answer

The security filter failed to detect an attack and the model returned an error

Answer

A non-adversarial input caused a NaN error due to missing data

Question 4

A security team discovers that an AI-based anomaly detection system frequently misclassifies benign network traffic as malicious when the source IP is from a specific geographic region. Which type of AI vulnerability is most likely being exploited?

Accepted Answer

Adversarial evasion. The system's biased behavior due to geographic region indicates an adversarial attack that exploits the model's sensitivity to certain features. Data poisoning would require alteration of training data, model inversion extracts training data, and membership inference determines if a record was in training set. The scenario describes an evasion attack using adversarial examples to cause misclassification.

Answer

Data poisoning

Answer

Model inversion

Answer

Membership inference

Question 5

A healthcare organization uses an AI model to recommend treatment plans. The model was trained on data from a single hospital, and now treats patients from multiple demographics. Which ethical concern is most critical?

Accepted Answer

Fairness and bias in predictions. Training on homogeneous data leads to biased predictions for underrepresented groups, raising fairness concerns. While transparency and explainability are important, fairness is the primary issue due to demographic shift. Privacy and accountability are secondary in this context.

Answer

Accountability for treatment outcomes

Answer

Lack of transparency in model decisions

Answer

Privacy violations in training data

Question 6

An AI system used for resume screening is found to consistently reject female candidates for technical roles. The data science team retrains the model after removing the 'gender' feature, but the bias persists. What is the most likely cause?

Accepted Answer

The model uses proxy variables that correlate with gender. Removing the gender feature alone is insufficient because other features (e.g., years of experience, education, hobbies) can act as proxies for gender. This is a common pitfall in fairness interventions.

Answer

The model architecture is too complex

Answer

The training data still contains historical hiring bias

Answer

The evaluation metric does not measure fairness

Question 7

A company implements an AI-based chatbot for customer service. After deployment, customers report that the chatbot sometimes uses offensive language. The development team reviews the training data and finds no explicit offensive content. What is the most likely explanation?

Accepted Answer

The model learned biased language patterns from the training corpus. Large language models can learn unintended associations from training data, including biased or offensive language embedded in context. Even without explicit offensive content, the model may generate such language due to learned patterns.

Answer

There is a bug in the deployment pipeline

Answer

The model is overfitting to rare examples

Answer

The training data was poisoned by an attacker

Question 8

An organization wants to ensure its AI systems comply with new regulations requiring explanations for automated decisions. Which governance practice is most directly relevant?

Accepted Answer

Deploying explainability tools. Explainability tools (e.g., SHAP, LIME) provide post-hoc explanations for model decisions, directly addressing the requirement for explanations. The other options are important but not as directly relevant to providing explanations.

Answer

Implementing differential privacy

Answer

Conducting bias audits

Answer

Establishing an AI ethics board

Question 9

A financial institution uses an AI model to approve loans. The model uses features including credit score and ZIP code. During an audit, it is discovered that the model has a high false positive rate for loan default predictions in certain ZIP codes. What should the institution do to address this?

Accepted Answer

Retrain the model with fairness constraints. The false positive disparity in certain ZIP codes likely indicates bias related to demographics. The best course is to retrain the model with fairness constraints to mitigate disparate impact while maintaining performance.

Answer

Remove the ZIP code feature from the model

Answer

Increase the decision threshold for those ZIP codes

Answer

Discontinue use of the model for those ZIP codes

Question 10

A team is deploying an AI model that predicts patient readmission risk. The model was trained on data from three hospitals but will be used in a fourth hospital with different patient demographics. What is the most important security risk to assess?

Accepted Answer

Adversarial attacks that cause misclassification. Using a model on data from a different distribution (population shift) can degrade performance, but from a security perspective, the main risk is adversarial attacks that exploit the model's unfamiliarity with new data. Model inversion and poisoning are training-time attacks; data breach is an operational risk but not specific to this scenario.

Answer

Data poisoning during training

Answer

Model inversion to extract patient data

Answer

Data breach of the inference API

Question 11

An AI development team is building a system to detect fraudulent transactions. They want to ensure the model complies with regulations requiring that individuals can question automated decisions. Which governance element is most relevant?

Accepted Answer

Right to explanation. The right to explanation is a key element of AI governance, requiring that individuals be given a reason for automated decisions. This directly relates to the scenario.

Answer

Model versioning

Answer

Differential privacy

Answer

Data minimization

Question 12

A security researcher demonstrates that by adding small perturbations to an image of a stop sign, an autonomous vehicle's AI misclassifies it as a speed limit sign. This is an example of which type of attack?

Accepted Answer

Adversarial example attack. Adding small perturbations to input to cause misclassification is a classic adversarial example attack, which falls under evasion attacks (adversarial machine learning). Poisoning alters training data, extraction steals model parameters, and inference determines membership.

Answer

Data poisoning attack

Answer

Model extraction attack

Answer

Membership inference attack

Question 13

Which TWO of the following are common methods for mitigating bias in AI models?

Accepted Answer

Reweighting training samples based on sensitive attributes. Reweighting training samples based on sensitive attributes is a common pre-processing bias mitigation technique. It assigns higher weights to underrepresented groups or lower weights to overrepresented groups to balance the dataset, thereby reducing the model's reliance on biased correlations. This method directly addresses data-level bias before model training begins.

Answer

Using adversarial training

Answer

Applying L1 regularization

Answer

Performing k-fold cross-validation

Question 14

Which THREE of the following are key principles of AI ethics as defined by major frameworks?

Accepted Answer

Transparency. Transparency, fairness, and accountability are core principles across many AI ethics frameworks. Scalability and latency are technical performance metrics, not ethics.

Answer

Scalability

Answer

Latency

Question 15

Which TWO of the following are effective defenses against adversarial evasion attacks on image classifiers?

Accepted Answer

Gradient masking. Adversarial training and gradient masking (e.g., defensive distillation) are common defenses. Data augmentation helps generalization but not specifically against adversarial perturbations; feature squeezing reduces input complexity; input validation is generic.

Answer

Data augmentation

Answer

Input validation

Answer

Feature squeezing

AI0-001 AI Security, Ethics and Governance Practice Test 3 — 15 Questions

Exhibit

AI0-001 AI Security, Ethics and Governance Practice Test 3 — 15 Questions

Exhibit