AI0-001 AI Security, Ethics and Governance Practice Test 5 — 15 Questions

Question 1

A healthcare AI system used for diagnosis shows a significant accuracy difference between demographic groups. Which technique should be applied to directly reduce this bias during model training?

Accepted Answer

Apply adversarial debiasing during training. Adversarial debiasing is a training-time technique that forces the model to learn features uncorrelated with demographic attributes, directly reducing bias.

Answer

Ignore the disparity as long as overall accuracy is acceptable

Answer

Retrain the model with more data from the underperforming group

Answer

Remove demographic attributes from the training data

Question 2

Which principle ensures that AI decisions can be traced back and understood by humans?

Accepted Answer

Transparency. Transparency refers to the ability to explain and understand how an AI system reached a decision.

Answer

Privacy

Answer

Robustness

Answer

Accountability

Question 3

A financial institution uses a deep learning model for loan approvals. Under the EU AI Act, this is considered a high-risk AI system. Which mandatory requirement must the institution fulfill before deployment?

Accepted Answer

Conduct a risk assessment and bias testing. High-risk AI systems under the EU AI Act require a conformity assessment, including a risk assessment and bias testing.

Answer

Obtain certification from an ISO 27001 auditor

Answer

Publish the model's source code publicly

Answer

Register the AI system with the national data protection authority

Question 4

An image classification model misclassifies a stop sign as a speed limit sign after a few pixels are altered. What is the most effective defense against such attacks?

Accepted Answer

Adversarial training. Adversarial training incorporates adversarial examples during training, making the model more robust.

Answer

Use a larger validation dataset

Answer

Reduce the input image resolution

Answer

Increase the model's complexity

Question 5

Which ethical concern is most directly associated with AI systems that fully automate decision-making without human oversight?

Accepted Answer

Loss of human autonomy. Lack of human oversight can lead to ethical issues like biased decisions or unfair outcomes without recourse.

Answer

Data storage costs

Answer

Job displacement

Answer

Environmental impact

Question 6

A research lab trains a language model using DP-SGD. What primary privacy risk does this technique mitigate?

Accepted Answer

Membership inference attacks. DP-SGD limits the memorization of individual training records, preventing reconstruction attacks.

Answer

Data poisoning attacks

Answer

Adversarial patch attacks

Answer

Model inversion attacks

Question 7

Which AI governance framework is specifically designed by the U.S. National Institute of Standards and Technology (NIST) to help organizations manage AI risks?

Accepted Answer

NIST AI Risk Management Framework. NIST AI Risk Management Framework (AI RMF) is a comprehensive framework for AI risk assessment and mitigation.

Answer

ISO/IEC 27001

Answer

COBIT

Answer

GDPR

Question 8

What is the primary function of an AI ethics board within an organization?

Accepted Answer

Reviewing AI projects for ethical compliance. An AI ethics board reviews AI projects to ensure they align with ethical principles and regulatory requirements.

Answer

Developing algorithms

Answer

Managing cloud infrastructure

Answer

Marketing AI products

Question 9

After deploying a model for fraud detection, the data scientist observes a steady decline in precision over two months. Which issue is most likely occurring?

Accepted Answer

Concept drift. Concept drift occurs when the statistical properties of the target variable change over time, reducing prediction accuracy.

Answer

Data drift

Answer

Model overfitting

Answer

Adversarial attack

Question 10

Which TWO are common types of adversarial attacks on AI models?

Accepted Answer

Evasion attack. Evasion attacks and data poisoning are well-known adversarial attack vectors.

Answer

Hyperparameter tuning

Answer

Transfer learning

Answer

Backdoor attack

Question 11

Which THREE are key principles of trustworthy AI according to the OECD?

Accepted Answer

Robustness. Transparency, accountability, and robustness are three of the five OECD principles (along with inclusive growth and human-centred values). Profitability and scalability are not principles.

Answer

Profitability

Answer

Scalability

Question 12

Which TWO techniques are specifically designed to protect individual privacy when training AI models?

Accepted Answer

Differential privacy. Differential privacy and anonymization are directly aimed at preserving privacy by limiting information leakage.

Answer

Dropout

Answer

Model pruning

Answer

Regularization

Question 13

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

Accepted Answer

Bias mitigation is needed. The demographic parity difference of 0.15 indicates potential bias against a demographic group, so bias mitigation is the most critical ethical concern.

Answer

Feature engineering should be improved

Answer

Data collection needs expansion

Answer

Model retraining is required due to low recall

Question 14

Refer to the exhibit. Which model is NOT in full compliance with the policy?

Accepted Answer

CreditScorer v2. CreditScorer v2 is listed in the exceptions, meaning it is not required to meet the policy requirements.

Answer

ChurnPredict v1

Answer

FraudDetect v4

Answer

LoanApproval v3

Question 15

A large hospital system deploys an AI triage system for emergency rooms. The system uses patient vitals and symptoms to recommend treatment priority. Six months after deployment, complaints arise that the system frequently underestimates the severity of symptoms for patients from certain ethnic backgrounds. A data scientist runs a bias audit and finds that the model's false negative rate is 20% higher for the minority group. The hospital's AI governance board requires immediate corrective action. The data science team has limited resources and cannot retrain the entire model from scratch. They have access to the training data, which is imbalanced. The model is a gradient boosted tree. Which course of action best addresses the bias while minimizing operational impact?

Accepted Answer

Post-process the model's predictions by adjusting thresholds for the minority group. Post-processing threshold adjustment is quick, does not require retraining, and directly reduces false negative disparities across groups.

Answer

Rebalance the training data using SMOTE and retrain the model

Answer

Use adversarial debiasing during training to remove protected attribute correlations

Answer

Replace the model with a simpler logistic regression model to improve interpretability