AI0-001 AI Security, Ethics and Governance Practice Test 6 — 15 Questions

Question 1

A security analyst is reviewing logs from an AI-powered recommendation system and notices an unusually high number of requests for products from a specific vendor. The analyst suspects data poisoning. Which mitigation strategy should be implemented first?

Accepted Answer

Implement input validation and sanitization for training data. Option B is correct because input validation and sanitization help prevent malicious data from entering the training pipeline. Option A is wrong because encryption does not detect or prevent data poisoning. Option C is wrong because anomaly detection is reactive, not proactive. Option D is wrong while retraining is part of the solution, the immediate step is to validate the input.

Answer

Encrypt all training data at rest

Answer

Deploy an anomaly detection system on model outputs

Answer

Retrain the model with a smaller, curated dataset

Question 2

A healthcare startup deploys an AI model to predict patient readmission rates. An internal audit reveals that the model consistently underestimates readmission risk for non-native English speakers. According to AI ethics principles, what is the most appropriate course of action?

Accepted Answer

Retrain the model with a more representative dataset that includes diverse language backgrounds. Option C is correct because the issue stems from biased training data; retraining with balanced data and including diverse patient data can reduce bias. Option A is wrong as ignoring the issue is unethical and may violate regulations. Option B is wrong because post-hoc explanations do not fix the underlying bias. Option D is wrong because reducing sample size may worsen bias.

Answer

Add a confidence score disclaimer to model outputs

Answer

Reduce the sample size of non-native English speakers to balance the dataset

Answer

Continue using the model as is, since overall accuracy is acceptable

Question 3

During a penetration test, a security engineer discovers that an AI-powered chatbot can be tricked into revealing sensitive customer data by using specially crafted prompts. What type of attack is this, and what is the best mitigation?

Accepted Answer

Prompt injection attack; implement input validation and context sanitization. Option D is correct: the attack is a prompt injection, and input validation (sanitization) is a primary defense. Option A is wrong because model inversion is different. Option B is wrong because this is not a membership inference attack. Option C is wrong because differential privacy is for training, not for prompt-level attacks.

Answer

Model inversion attack; apply differential privacy during training

Answer

Data poisoning attack; implement strict access controls

Answer

Membership inference attack; add noise to model outputs

Question 4

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. Option D is correct: a bias audit and fairness assessment should be conducted to quantify impact. Option A is wrong because without analysis, altering thresholds is arbitrary. Option B is wrong because equalizing rates may lead to reverse discrimination or mask root causes. Option C is wrong because full disclosure may expose the company to liability without remediation.

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 5

An organization deploys an AI system that processes personal data of EU citizens. Which regulatory framework imposes strict requirements on automated decision-making and profiling?

Accepted Answer

General Data Protection Regulation (GDPR). Option A is correct: the GDPR provides rules on automated decision-making and profiling. Option B is wrong because HIPAA is for healthcare data in the US. Option C is wrong because PCI DSS is for payment card data. Option D is wrong because SOX is for financial reporting.

Answer

Payment Card Industry Data Security Standard (PCI DSS)

Answer

Health Insurance Portability and Accountability Act (HIPAA)

Answer

Sarbanes-Oxley Act (SOX)

Question 6

Which TWO of the following are common techniques to improve the transparency and interpretability of an AI model?

Accepted Answer

Generate SHAP (SHapley Additive exPlanations) values. Options B and D are correct. SHAP values and LIME are model-agnostic interpretability methods. Option A is wrong because deep learning models are often black boxes. Option C is wrong as random forest is a specific model, not a technique. Option E is wrong because differential privacy is for privacy, not interpretability.

Answer

Use differential privacy to add noise to training data

Answer

Implement a random forest algorithm

Answer

Use deep neural networks to increase model complexity

Question 7

Which TWO of the following are effective defenses against adversarial examples in AI systems?

Accepted Answer

Train the model with adversarial examples (adversarial training). Options B and D are correct. Adversarial training and input sanitization/denoising are defenses. Option A is wrong because model compression may remove defenses. Option C is wrong because increasing model sensitivity can make it more vulnerable. Option E is wrong because ensemble voting is not a direct defense against adversarial examples.

Answer

Use an ensemble of models and majority voting

Answer

Increase the model's sensitivity to input changes

Answer

Reduce model complexity through pruning

Question 8

Which THREE of the following are key principles of trustworthy AI as defined by major regulatory bodies?

Accepted Answer

Fairness and non-discrimination. Options A, C, and D are correct. Accountability, fairness, and transparency are core principles. Option B is wrong because maximum profitability is not an ethical principle. Option E is wrong because proprietary secrecy conflicts with transparency.

Answer

Maximum profitability

Answer

Proprietary secrecy

Question 9

A large e-commerce company uses a recommendation engine trained on millions of user interactions. Recently, the marketing team noticed a sharp increase in click-through rates for a particular product category. Upon investigation, an engineer found that a competitor had injected fake user profiles that consistently clicked on their products, skewing the training data. The company needs to remediate the attack and prevent future occurrences. The team has limited time and budget. Which course of action should the company take first?

Accepted Answer

Identify and remove the fake user profiles from the training dataset, then retrain the model. Option A is correct because removing the injected data and retraining directly removes the poison and restores model integrity. Option B is wrong because adversarial training focuses on evasion attacks, not poisoning. Option C is wrong because adding noise does not remove existing poisoned data. Option D is wrong because reducing retraining frequency does not address the attack and could allow more damage.

Answer

Implement adversarial training to make the model robust to future poisoning attempts

Answer

Decrease the frequency of model retraining to limit exposure to new data

Answer

Add differential privacy noise to the training data to mask the injected profiles

Question 10

A hospital deploys an AI diagnostic assistant that analyzes medical images. The system has been in use for six months, and radiologists have reported that the AI is increasingly confident in its predictions, but sometimes misses rare conditions. The AI ethics board is concerned about overreliance and potential harm from false negatives. They want to implement a governance framework that ensures appropriate human oversight. The hospital has a limited IT budget. What is the best approach?

Accepted Answer

Implement a human-in-the-loop process where the AI flags low-confidence or rare condition predictions for mandatory radiologist review. Option C is correct because establishing a human-in-the-loop process for high-risk cases ensures oversight without full automation removal. Option A is wrong because requiring all cases to be reviewed eliminates efficiency gains. Option B is wrong because increasing false positive threshold could increase false negatives. Option D is wrong because confidence scores alone are not sufficient; mandatory human review for low-confidence cases is needed.

Answer

Add a warning to the AI interface that says 'This tool may miss rare conditions'

Answer

Require all AI predictions to be reviewed by a radiologist before final diagnosis

Answer

Increase the AI's false positive threshold to reduce missed cases

Question 11

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. Option D is correct because the attack exploits the reporting system; redesigning it to detect coordinated behavior reduces poisoning. Option A is wrong because retraining on non-reported data may not capture all necessary patterns. Option B is wrong because ignoring reported data removes a valuable signal. Option C is wrong because adversarial training is not designed for this type of poisoning.

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 12

A credit union uses an AI model to approve personal loans. The model was trained on historical data from the past five years. A recent internal review shows that the model approves loans predominantly for white applicants compared to other ethnicities, even when income and credit scores are similar. The credit union wants to comply with fair lending laws without significantly reducing overall approval rates. The data science team has access to the training data. What is the most appropriate remediation step?

Accepted Answer

Resample the training data to ensure balanced representation of ethnicities. Option B is correct because resampling (oversampling minority groups or undersampling majority) can balance the representation and reduce bias. Option A is wrong because equalizing rates without addressing data bias may not be sustainable. Option C is wrong because skipping the model is not practical. Option D is wrong because simple reweighting may not correct complex biased patterns.

Answer

Apply a fairness constraint that penalizes the model for disparate impact

Answer

Discontinue the AI model and use manual approval for all loans

Answer

Adjust the approval threshold so that approval rates are equal across ethnic groups

Question 13

A national security agency uses AI to analyze surveillance data for threat detection. The system is deployed in a high-stakes environment where false negatives could lead to missed threats, and false positives waste analyst time. Recently, a known hacker group attempted to evade detection by subtly modifying their communication patterns over time, a form of adversarial evasion. The agency wants to harden the system while maintaining performance. The system uses a deep neural network. Which mitigation strategy is most appropriate?

Accepted Answer

Perform adversarial training using the hacker group's known evasion patterns. Option C is correct because adversarial training exposes the model to known evasion patterns during training, improving robustness without changing the model type. Option A is wrong because reducing model complexity may decrease accuracy. Option B is wrong because unsupervised learning may not capture the specific adversarial patterns. Option D is wrong because random input perturbations do not represent realistic evasion.

Answer

Switch to an unsupervised learning approach to detect anomalies

Answer

Simplify the model to a logistic regression to reduce the attack surface

Answer

Add random noise to all input data to confuse evasion attempts

Question 14

A startup develops an AI recruiting tool that screens resumes. After deployment, they receive a complaint from a candidate who claims the system rejected them due to age discrimination. The startup has no formal AI governance process. They want to quickly assess and remediate the issue. The dataset includes age as a feature. What should they do first?

Accepted Answer

Conduct a bias analysis to measure the model's impact on different age groups. Option B is correct because the complaint suggests age bias; the first step is to analyze the model's predictions across age groups to verify if there is bias. Option A is wrong because removing age may not eliminate proxies. Option C is wrong because disregarding the complaint is unethical. Option D is wrong without first understanding the bias source.

Answer

Apologize to the candidate and offer a manual review of their resume

Answer

Immediately remove age from the feature set and retrain the model

Answer

Ignore the complaint because age is a legitimate business requirement

Question 15

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. Option D is correct because reverting to the last good model and then cleaning the data ensures the corruption is removed. Option A is wrong because retraining with both corrupted and good data may not remove the influence. Option B is wrong because reweighting may not fully correct. Option C is wrong because using a simpler model may not capture complex patterns.

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