AI0-001 Practice Test 7 — 10 Questions

Question 1

A team trains a neural network for image classification. During training, the loss decreases on the training set but increases on the validation set after a few epochs. What is the most likely cause?

Accepted Answer

Overfitting. Overfitting occurs when the model learns the training data too well, including noise and irrelevant patterns, causing it to memorize rather than generalize. This is evidenced by the loss decreasing on the training set while increasing on the validation set after a few epochs, as the model's performance on unseen data degrades.

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Vanishing gradients

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Incorrect learning rate scheduling

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Underfitting

Question 2

An organisation is developing an AI policy. According to the NIST AI RMF, which function involves establishing policies and procedures to ensure the organisation governs AI responsibly?

Accepted Answer

Govern. The Govern function of the NIST AI RMF establishes policies, procedures, and organizational structures to ensure AI is developed and used responsibly. It is the foundational function that cultivates a risk-management culture and assigns accountability across the AI lifecycle.

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Manage

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Measure

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Map

Question 3

Which machine learning paradigm involves training an agent to make decisions by interacting with an environment and receiving rewards or penalties based on its actions?

Accepted Answer

Reinforcement learning. Reinforcement learning (RL) is the correct paradigm because it explicitly involves an agent learning a policy through trial-and-error interactions with an environment, receiving scalar reward signals (positive or negative) to maximize cumulative reward. This matches the question's description of making decisions based on rewards or penalties, which is the defining characteristic of RL, as opposed to learning from labeled data or discovering hidden patterns without feedback.

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Unsupervised learning

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Supervised learning

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Self-supervised learning

Question 4

A company uses an LLM API to generate customer support responses. They want to prevent the LLM from generating harmful content, even when users attempt jailbreaking. Which defense is MOST effective at the application layer?

Accepted Answer

Output filtering and content moderation. Output filtering and content moderation is the most effective defense at the application layer because it directly inspects the LLM's generated response before it reaches the user. This approach can catch and block harmful content that results from successful jailbreaking attempts, which input validation alone cannot prevent since the model may still produce undesirable outputs even with sanitized inputs.

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Input validation and sanitization

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Robust training techniques

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Rate limiting

Question 5

A company wants to roll out a new recommendation model to production. They decide to run an A/B test where 10% of users see the new model and 90% see the old model. After one week, the new model shows a 5% improvement in click-through rate. What is the next best action?

Accepted Answer

Increase the testing percentage gradually while monitoring performance metrics and guardrails. A 5% improvement observed over only one week with a 10% traffic split is insufficient to confirm statistical significance or rule out novelty effects, data drift, or seasonal bias. The recommended best practice in AI deployment is to gradually increase the testing percentage (e.g., 10% → 25% → 50% → 100%) while continuously monitoring performance metrics and guardrails (e.g., click-through rate, conversion rate, latency, and error rates) to ensure the new model generalizes safely across the full user population.

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Immediately roll out the new model to 100% of users

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Revert to the old model because the improvement is minimal

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Run the test for another month to ensure statistical significance

Question 6

A financial institution deploys an AI credit scoring model. After six months, the model's performance drops significantly. Analysis shows that the relationship between features and labels has changed. Which term describes this phenomenon?

Accepted Answer

Concept drift. Concept drift occurs when the statistical relationship between input features and the target label changes over time, which is exactly what happened when the credit scoring model's performance dropped due to a shift in the feature-label relationship. This is distinct from data drift, which only involves changes in the input data distribution without affecting the label mapping.

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Model decay

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Overfitting

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Data drift

Question 7

A company deploys an LLM-based chatbot that retrieves data from external databases. An attacker embeds malicious instructions in a database record. When the chatbot retrieves that record, it executes the instructions, overriding its system prompt. Which type of attack is this?

Accepted Answer

Indirect prompt injection. This is an indirect prompt injection attack because the malicious instructions are embedded in a third-party data source (the database record) rather than being sent directly by the user. When the LLM retrieves and processes that record, the injected instructions override the system prompt, causing the chatbot to behave contrary to its intended design.

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Model inversion attack

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Direct prompt injection

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Membership inference attack

Question 8

In the AI lifecycle, which phase involves splitting data into training, validation, and test sets?

Accepted Answer

Data preprocessing. Data preprocessing is the phase where raw data is cleaned, transformed, and prepared for modeling. Splitting the dataset into training, validation, and test sets is a critical step during this phase to ensure unbiased evaluation and prevent data leakage. This split occurs before any model training begins, making it part of preprocessing rather than training or evaluation.

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Model training

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Data collection

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Model evaluation

Question 9

A security team is evaluating the risk of adversarial examples against their image classification system. Which of the following BEST describes an adversarial example?

Accepted Answer

An input crafted with small, intentional perturbations that cause the model to output an incorrect prediction. An adversarial example is specifically an input that has been deliberately modified with small, often imperceptible perturbations to cause a machine learning model to misclassify it. This exploits the model's sensitivity to high-dimensional input spaces, where tiny changes in pixel values can shift the decision boundary without altering human perception of the image.

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A technique that reconstructs training data from the model's outputs

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An attack that injects malicious data into the training set to corrupt the model

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A method to determine if a specific data point was used in the training set

Question 10

A security team is conducting a red team exercise on a new LLM-powered customer support system. Which activity is part of red teaming?

Accepted Answer

Attempting to jailbreak the LLM to bypass safety guardrails. Red teaming in the context of an LLM-powered system involves actively probing for security vulnerabilities, such as attempting to bypass safety guardrails through jailbreak prompts. Option A directly describes this adversarial testing, which is the core activity of a red team exercise to identify weaknesses before malicious actors can exploit them.

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Reviewing the model's training data for bias

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Implementing access controls on the model API

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Monitoring system performance metrics