AI0-001 Practice Test 20 — 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 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 3

A company deployed an AI chatbot that started generating offensive responses after a data update. The security team needs to quickly mitigate the issue. What should they do first?

Accepted Answer

Disable the chatbot and investigate. Option A (Disable the chatbot and investigate) is correct because immediate action to stop harm is critical, then root cause analysis. Option B (Roll back to previous model version) might work if the update caused it, but disabling first is safer. Option C (Add a content filter) may not catch all offensive responses. Option D (Delete the training data) is irreversible and extreme.

Answer

Delete the training data

Answer

Roll back to previous model version

Answer

Add a content filter

Question 4

Which TWO are common attack vectors against AI systems? (Choose two.)

Accepted Answer

Data poisoning. Options B (Adversarial examples) and D (Data poisoning) are correct. Adversarial examples manipulate inputs to cause misclassification; data poisoning corrupts training data. Option A (SQL injection) is a traditional attack. Option C (Buffer overflow) is a software vulnerability. Option E (Cross-site scripting) is a web attack.

Answer

SQL injection

Answer

Cross-site scripting

Answer

Buffer overflow

Question 5

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 6

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 7

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 8

An organization wants to implement an AI ethics board. Which composition best ensures independence and expertise?

Accepted Answer

Mix of internal stakeholders and external ethicists. Option B (Mix of internal stakeholders and external ethicists) is correct because external ethicists provide unbiased perspective and expertise, while internal stakeholders understand operations. Option A (Only senior executives from the company) may have conflicts of interest. Option C (All members from the legal department) focuses only on compliance. Option D (IT department head and data scientists) lacks ethics expertise.

Answer

All members from the legal department

Answer

IT department head and data scientists

Answer

Only senior executives from the company

Question 9

Refer to the exhibit. An auditor reports that the model's fairness check was bypassed in a recent deployment. Based on the policy, what is the most likely cause?

Accepted Answer

Data scientist role has deploy permission, allowing deployment without fairness validation. Option B (Data scientist role has deploy permission, allowing deployment without fairness validation) is correct. The policy shows fairness_check required, but if the deployment process does not enforce it, the data scientist could bypass it. Option A (Auditor lacks evaluate) is unrelated to deployment. Option C (Fairness threshold low) does not cause bypass. Option D (External user inference) is unrelated.

Answer

The auditor role lacks 'evaluate' permission

Answer

Fairness check threshold is set to 0.8, which is too low

Answer

External_user role can perform inference, which triggers unfair predictions

Question 10

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 11

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 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

An organization uses an AI-based hiring tool. To prevent bias, they want to ensure the model's decisions are explainable. Which approach is most suitable?

Accepted Answer

Use a simpler interpretable model like logistic regression. Option D (Use a simpler interpretable model like logistic regression) is correct because simple models are inherently interpretable. Option A (Use a black-box deep learning model with SHAP) still lacks full interpretability. Option B (Use ensemble methods with feature importance) is complex. Option C (Use reinforcement learning with fairness constraints) is hard to explain.

Answer

Use reinforcement learning with fairness constraints

Answer

Use a black-box deep learning model with SHAP explanations

Answer

Use ensemble methods with feature importance

Question 14

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 15

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