AI0-001 Practice Test 3 — 25 Questions

Question 1

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?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Reduced model accuracy for increased privacy. Differential privacy works by adding calibrated noise to the training process or model outputs, which directly reduces the model's accuracy in exchange for a quantifiable privacy guarantee (e.g., ε-differential privacy). This trade-off is fundamental: stronger privacy (lower ε) requires more noise, which degrades predictive performance. The healthcare organization must balance the need to protect patient data against the clinical utility of accurate readmission predictions.

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Increased training time for reduced bias

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Lower interpretability for higher fairness

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Faster inference for lower memory usage

Question 2

An e-commerce company deploys a recommendation system using collaborative filtering. After launch, the system shows high accuracy for popular items but fails to recommend niche products to users who would likely buy them. Which technique should the team implement to improve recommendations for long-tail items?

Accepted Answer

Switch to a hybrid filtering approach that incorporates item metadata. Collaborative filtering relies on user-item interactions, which are sparse for niche products (the long tail). A hybrid filtering approach that incorporates item metadata (e.g., category, description, attributes) can bridge the gap by using content-based signals to recommend niche items even when interaction data is limited. This directly addresses the cold-start and sparsity problems for long-tail items.

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Apply matrix factorization with higher latent factors

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Increase the weight of popular items in the recommendation score

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Collect more user interaction data over time

Question 3

A hospital uses an AI system to prioritize patient triage based on vital signs and medical history. During a trial, the system consistently assigns lower urgency to elderly patients with chronic conditions, even when their symptoms suggest high risk. Which approach best addresses this bias?

Accepted Answer

Audit the training data for representation of elderly patients and retrain with balanced data. The bias originates from the training data underrepresenting elderly patients with chronic conditions, causing the model to learn skewed urgency patterns. Auditing the data for representation and retraining with balanced data directly addresses the root cause by ensuring the model learns from a fair distribution of cases, which is a standard bias mitigation technique in AI systems.

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Use a different dataset from a similar hospital without checking demographics

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Manually increase the weight of age-related features in the model

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Replace the neural network with a decision tree to simplify decision logic

Question 4

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. The chatbot's offensive language likely stems from biased or toxic patterns present in the training corpus, even if no explicit offensive content was flagged. Large language models learn statistical associations from their training data, and if the corpus contains subtle biases, stereotypes, or indirect toxic language, the model can reproduce these patterns in its responses. This is a well-known issue in AI ethics and governance, where models inadvertently amplify societal biases embedded in the data.

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There is a bug in the deployment pipeline

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The model is overfitting to rare examples

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The training data was poisoned by an attacker

Question 5

Which THREE factors are common causes of bias in AI systems?

Accepted Answer

Lack of diversity in the development team. A lack of diversity in the development team leads to homogeneity of thought, which can cause blind spots in identifying potential biases in data, features, or model behavior. When the team does not represent the full spectrum of end users, the AI system may inadvertently encode assumptions that disadvantage underrepresented groups, resulting in biased outcomes.

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Cross-validation

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High regularization

Question 6

Which technique adds controlled noise to query results or training data to prevent an attacker from inferring whether a specific individual's data was included in the dataset?

Accepted Answer

Differential privacy. Differential privacy is the technique that adds controlled statistical noise (e.g., Laplace or Gaussian noise) to query results or training data so that the presence or absence of any single individual's data cannot be inferred. It provides a mathematical guarantee (epsilon-differential privacy) that the output distribution is nearly identical whether or not a specific record is included. This directly matches the question's requirement.

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Anonymisation

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Federated learning

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Pseudonymisation

Question 7

A team is training a deep learning model for natural language processing using a large corpus. They notice the model has a very high number of parameters and training is slow. Which technique can reduce the number of parameters without significant performance loss?

Accepted Answer

Apply embedding compression. Embedding compression reduces the dimensionality of the embedding layer, which often contains the majority of the model's parameters in NLP tasks. By using techniques like low-rank factorization or pruning, the model retains most of its representational power while significantly decreasing the parameter count and training time.

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Add more dropout layers

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Use a larger batch size

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Increase learning rate

Question 8

A research team is training a deep neural network for image classification. The training loss decreases rapidly for the first few epochs but then plateaus, while validation loss starts to increase after epoch 10. Which action would best address this issue?

Accepted Answer

Implement early stopping based on validation loss to prevent further overfitting.. The training loss decreasing rapidly then plateauing while validation loss increases after epoch 10 is a classic sign of overfitting. Early stopping monitors validation loss and halts training when it begins to rise, preventing the model from memorizing noise in the training data. This directly addresses the overfitting issue without requiring architectural or hyperparameter changes that could destabilize training.

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Reduce the batch size to introduce more noise during training.

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Increase the learning rate to help the model escape the plateau.

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Add more convolutional layers to increase model capacity.

Question 9

A data scientist is training a model to detect fraudulent transactions. To protect customer privacy, the team wants to ensure that the model does not inadvertently memorize and reveal sensitive information about individuals in the training set. Which technique should be applied during training?

Accepted Answer

Differential privacy. Differential privacy is the correct technique because it adds calibrated noise to the training process or output, ensuring that the model cannot infer whether any specific individual's data was included in the training set. This directly addresses the goal of preventing memorization and leakage of sensitive information while still allowing the model to learn useful patterns for fraud detection.

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Federated learning

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Homomorphic encryption

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Model quantization

Question 10

A data scientist needs to predict whether a customer will churn (yes/no) based on historical data. Which type of machine learning problem is this?

Accepted Answer

Binary classification. This is a binary classification problem because the target variable has exactly two discrete outcomes: 'yes' (churn) or 'no' (no churn). Classification algorithms such as logistic regression, decision trees, or support vector machines are used to assign input features to one of these two predefined classes. The output is a categorical label, not a continuous value or a reward signal.

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Reinforcement learning

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Regression

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Clustering

Question 11

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 12

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 13

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 14

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 15

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 16

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 17

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 18

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 19

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 20

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