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

Question 1

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

Answer

The model weights were corrupted during loading

Answer

The model encountered an unknown data type

Question 2

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. Option A is correct because 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.

Answer

Increase the number of model replicas to distribute the load

Answer

Retrain the model with adversarial examples

Answer

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

Question 3

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.

Answer

Ignore the requirement as the model is proprietary

Answer

Ask the vendor to develop a custom explanation module

Answer

Replace the model with a simpler, interpretable model

Question 4

A security analyst notices that an AI model used for facial recognition is returning unusually high confidence scores for certain individuals while consistently misidentifying others. Which type of attack is most likely occurring?

Accepted Answer

Model inversion attack. Option C is correct because a model inversion attack aims to reconstruct training data by exploiting confidence scores, leading to overconfidence on familiar data. Option A is wrong because poisoning corrupts training data, not inference behavior. Option B is wrong because evasion attacks craft adversarial inputs to cause misclassification, not systematic overconfidence. Option D is wrong because extraction attacks steal model parameters through queries, not cause confidence anomalies.

Answer

Data poisoning

Answer

Evasion attack

Answer

Model extraction attack

Question 5

A bank deploys an AI system to approve loan applications. During testing, the model denies a disproportionate number of applicants from a particular demographic group, even after controlling for credit history. Which ethical principle is being violated?

Accepted Answer

Fairness. Option B is correct because fairness requires that AI systems do not discriminate against protected groups. Option A is wrong because transparency is about explainability, not bias. Option C is wrong because accountability refers to responsibility for outcomes, not the specific bias issue. Option D is wrong because privacy concerns data protection, not discrimination.

Answer

Transparency

Answer

Privacy

Answer

Accountability

Question 6

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. Option D is correct because timestamp, input/output data, and model version together provide full traceability for audit. Option A is wrong because logging only outputs and confidence is insufficient without inputs. Option B is wrong because logging only inputs and model name misses version and outputs. Option C is wrong because code and training data logs are not typically part of inference audit trails.

Answer

Input data and model name only

Answer

Source code and training dataset hash

Answer

Model outputs and confidence scores only

Question 7

A healthcare organization uses an AI model to predict patient readmission risk. To comply with patient privacy regulations, they apply differential privacy during training. What is the primary trade-off of using differential privacy?

Accepted Answer

Reduced model accuracy for increased privacy. Option A is correct because differential privacy adds noise, which reduces model accuracy but protects privacy. Option B is wrong because training time may increase slightly but not primarily; the main trade-off is accuracy. Option C is wrong because interpretability is not directly affected. Option D is wrong because inference latency is not significantly impacted.

Answer

Increased training time for reduced bias

Answer

Lower interpretability for higher fairness

Answer

Faster inference for lower memory usage

Question 8

A cybersecurity analyst monitors an AI chatbot that frequently produces offensive responses when given specific prompts. The development team suspects an adversarial attack. Which mitigation strategy is most effective against such prompt injection attacks?

Accepted Answer

Implement input validation and sanitization. Option C is correct because input validation and filtering can detect and block malicious prompts. Option A is wrong because retraining on clean data does not prevent injection attacks. Option B is wrong because reducing model size is unrelated. Option D is wrong because encryption protects data in transit, not input processing.

Answer

Retrain the model on a larger, curated dataset

Answer

Encrypt all communication between users and the chatbot

Answer

Reduce the model's number of parameters

Question 9

A company's AI governance board requires each model to have a model card documenting intended use, performance metrics, and limitations. What is the primary purpose of a model card?

Accepted Answer

To provide transparent documentation of model capabilities and limitations. Option B is correct because model cards communicate key information to stakeholders for informed use and transparency. Option A is wrong because model cards are not legal contracts. Option C is wrong because model cards are documentation, not training methodology. Option D is wrong because they describe limitations but not a full risk assessment framework.

Answer

To specify the exact training algorithm and hyperparameters

Answer

To outline a complete risk assessment framework

Answer

To serve as a legal contract between developers and users

Question 10

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). Option D is correct because SHAP values provide per-feature attribution for individual predictions. Option A is wrong because LIME is also for local explanations, but SHAP is more theoretically grounded and common for feature attribution. Option B is wrong because t-SNE is for visualization of high-dimensional data, not explanation. Option C is wrong because decision trees are a model type, not an explanation method for any model.

Answer

Decision tree surrogate model

Answer

Principal component analysis

Answer

t-SNE dimensionality reduction

Question 11

An AI system in a self-driving car misinterprets a stop sign due to a small sticker placed on it. This is an example of which security vulnerability?

Accepted Answer

Adversarial example attack. Option A is correct because adversarial perturbations (stickers) cause misclassification. Option B is wrong because data poisoning corrupts training data, not inference. Option C is wrong because model stealing extracts model parameters. Option D is wrong because supply chain attacks compromise components earlier.

Answer

Supply chain attack

Answer

Model inversion attack

Answer

Data poisoning attack

Question 12

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. Option C is correct because providing meaningful information about the decision logic aligns with GDPR Article 22. Option A is wrong because anonymization is for privacy, not explanation. Option B is wrong because consent is for data processing, not explanation. Option D is wrong because a human-in-the-loop is a safeguard but not a direct explanation.

Answer

Obtain explicit consent from candidates

Answer

Anonymize all candidate data before processing

Answer

Always have a human review the AI's recommendations

Question 13

Which TWO of the following are effective techniques for detecting bias in an AI model?

Accepted Answer

Fairness metrics such as equal opportunity difference. Options A and B are correct because disparate impact analysis measures outcome differences across groups, and fairness metrics quantify bias. Option C is wrong because cross-validation assesses generalization, not fairness. Option D is wrong because confusion matrices for all groups can reveal bias but are less direct than metrics designed for fairness. Option E is wrong because feature importance may not directly reveal bias.

Answer

Feature importance scores

Answer

Confusion matrix on the entire dataset

Answer

Cross-validation accuracy

Question 14

Which THREE of the following are key components of an AI governance framework?

Accepted Answer

Risk assessment and mitigation plans. Options A, B, and D are correct because risk assessment, data management, and transparency are core pillars. Option C is wrong because model accuracy is a performance metric, not a governance component. Option E is wrong because cloud infrastructure is deployment, not governance.

Answer

Cloud infrastructure configuration

Answer

Model accuracy benchmarks

Question 15

Which TWO of the following are common threats to AI model security?

Accepted Answer

Data poisoning. Options C and D are correct because adversarial examples and data poisoning are classic AI attacks. Option A is wrong because SQL injection targets databases, not models. Option B is wrong because phishing targets humans. Option E is wrong because DDoS targets network availability.

Answer

SQL injection

Answer

Distributed denial-of-service (DDoS)

Answer

Phishing attacks

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

Exhibit

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

Exhibit