AI0-001 Practice Test 17 — 15 Questions

Question 1

A natural language processing (NLP) team is building a sentiment analysis model. The raw text data contains punctuation, stop words, and URLs. Which TWO preprocessing steps are most appropriate to improve model performance? (Choose two.)

Accepted Answer

Remove all punctuation and URLs. Options A and B are correct. Removing stop words reduces noise, and removing punctuation and URLs helps standardize text. Option C (stemming) is useful but not always necessary; Option D (tokenization) is fundamental but the question asks for specific preprocessing to improve performance; Option E (converting to lowercase) is standard but not among the best two for this scenario; however, converting to lowercase is a basic step. Actually, typical preprocessing includes lowercasing, removing punctuation, removing stop words, and tokenization. The question asks for 'most appropriate'. Lowercasing is common but technically could be considered basic. I'll choose A and B as the two best. But to be safe, let's adjust: Many might argue lowercasing is essential. I'll make correct options A and D? Let's think: The stem says 'improve model performance'. Stop word removal and punctuation removal are often performed. Lowercasing is also standard. But I need exactly two. I'll go with A (remove stop words) and B (remove punctuation and URLs) as they directly reduce noise. Lowercasing is also important but often done anyway. I'll keep A and B.

Answer

Apply stemming to reduce words to root forms

Answer

Convert all text to lowercase

Answer

Tokenize the text into individual words

Question 2

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 3

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 4

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 5

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 6

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 7

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 8

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 9

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 10

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 11

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 12

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 13

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 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

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