AI0-001 Practice Test 33 — 15 Questions

Question 1

A cybersecurity team is red-teaming their internal LLM-powered code assistant. They want to test the model's resistance to jailbreaking techniques that bypass safety guardrails. Which TWO of the following should they include in their red teaming exercise to effectively evaluate jailbreak resilience?

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

Accepted Answer

Role-playing scenarios where the model is asked to act as a character with no restrictions (e.g., DAN). Role-playing scenarios (e.g., DAN) and encoding obfuscation (e.g., base64) are common jailbreak techniques. Payload splitting is a type of prompt injection, not specifically jailbreaking. Few-shot prompting is a legitimate technique. Model inversion is a privacy attack.

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Model inversion to reconstruct training data

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Payload splitting across multiple user messages

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Few-shot prompting with benign examples

Question 2

An organization is evaluating a third-party large language model to integrate into their customer-facing application. As part of supply chain security, which THREE steps should they take to vet the model before deployment?

Accepted Answer

Conduct security testing, including red teaming, to identify vulnerabilities in the model. Reviewing the model card and documentation, obtaining a software bill of materials (SBOM) for AI components, and performing security testing (red teaming) are key steps for supply chain security. Model inversion is an attack, not a vetting step. Federated learning is a training technique, not a vetting process.

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Use federated learning to retrain the model on internal data

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Run a model inversion attack on the model to verify training data privacy

Question 3

A data scientist is cleaning a dataset. Which TWO actions are appropriate for handling missing data?

Accepted Answer

Use a predictive model to estimate missing values.. Using a predictive model to estimate missing values is a sophisticated imputation technique that leverages relationships between features to fill gaps, preserving data integrity and avoiding bias. This approach is particularly useful when data is not missing completely at random, as it can capture complex patterns that simpler methods miss.

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Ignore missing values and train the model directly.

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Impute missing values with the mean of the entire dataset.

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Replace missing values with the most frequent value always.

Question 4

An organization deploys an AI system that processes personal data of EU citizens. Which regulatory framework imposes strict requirements on automated decision-making and profiling?

Accepted Answer

General Data Protection Regulation (GDPR). The General Data Protection Regulation (GDPR) is the correct regulatory framework because it specifically governs the processing of personal data of EU citizens and imposes strict requirements on automated decision-making and profiling under Article 22. This article grants individuals the right not to be subject to a decision based solely on automated processing, including profiling, which produces legal effects or similarly significant effects. The GDPR also mandates data protection impact assessments and transparency obligations for such AI-driven processing.

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Payment Card Industry Data Security Standard (PCI DSS)

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Health Insurance Portability and Accountability Act (HIPAA)

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Sarbanes-Oxley Act (SOX)

Question 5

A data scientist is evaluating a logistic regression model for binary classification on highly imbalanced data. Which TWO metrics are most appropriate to assess model performance? (Choose TWO.)

Accepted Answer

Recall. Recall (B) is correct because in highly imbalanced binary classification, the minority class (e.g., fraud or disease) is the focus. Recall measures the proportion of actual positives correctly identified, which is critical when missing a positive has high cost. Precision (C) is correct because it measures the proportion of predicted positives that are truly positive, which is essential when false positives are costly or when the model's positive predictions must be trustworthy.

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Accuracy

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Mean squared error (MSE)

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F1 score

Question 6

A data scientist is monitoring a deployed image classification model. Which TWO actions are best practices for detecting model drift? (Choose 2.)

Accepted Answer

Use a holdout test set to periodically evaluate model accuracy.. Periodically evaluating the model on a holdout test set that reflects the current production data distribution is a direct method to detect accuracy degradation caused by model drift. This approach measures whether the model's performance on unseen data has declined over time, which is a key indicator of drift.

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Schedule automatic weekly retraining of the model.

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Increase the model's complexity to improve generalization.

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Monitor the average prediction confidence of the model.

Question 7

Refer to the exhibit. An AI governance review finds that a model was deployed without required ethics approval. Based on the audit log, who is most responsible for the compliance failure?

Accepted Answer

Bob. Bob is the data scientist who deployed the model to production. The audit log shows that Bob executed the deployment command without first obtaining the required ethics approval. As the individual who performed the action that violated the governance policy, Bob bears primary responsibility for the compliance failure.

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Alice

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Carol

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System

Question 8

A company is developing an AI chatbot for customer service. They want to ensure the bot does not generate offensive or harmful responses. Which governance practice should be implemented first?

Accepted Answer

Implement a content filter to screen responses before delivery. A content filter acts as a real-time safety gate that screens every response generated by the AI model before it reaches the customer. This is the first line of defense against offensive or harmful outputs, as it can catch toxic language, PII leaks, or policy violations immediately, even if the underlying model has not been fully sanitized. Without such a filter, harmful responses could be delivered before any other governance measure (like human review or policy creation) can intervene.

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Set up a human-in-the-loop review process

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Create a usage policy for acceptable bot behavior

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Sanitize training data to remove toxic examples

Question 9

Refer to the exhibit. The model is a neural network for 10-class classification. The training log shows no improvement over 5 epochs. Which of the following is the most likely root cause?

Accepted Answer

The model is suffering from vanishing gradients, preventing weight updates.. The training log shows no improvement over 5 epochs, which is a classic symptom of vanishing gradients in deep neural networks. When gradients become extremely small during backpropagation, weight updates are negligible, causing the loss to stagnate. This is especially common in deep networks with sigmoid or tanh activations, where gradients saturate in the tails of the activation function.

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The batch size is too large, making gradient updates insignificant.

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The output layer uses sigmoid activation instead of softmax.

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The learning rate is too high, causing the loss to oscillate.

Question 10

An AI development team is building a system to detect fraudulent transactions. They want to ensure the model complies with regulations requiring that individuals can question automated decisions. Which governance element is most relevant?

Accepted Answer

Right to explanation. The right to explanation is a governance principle that requires automated decision-making systems to provide individuals with meaningful information about how decisions are made. In the context of fraudulent transaction detection, this regulation ensures that a customer can question why a transaction was flagged, and the model must be able to provide an interpretable rationale. This directly aligns with the scenario's requirement for compliance with regulations allowing individuals to question automated decisions.

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Model versioning

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Differential privacy

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Data minimization

Question 11

A machine learning engineer needs to containerize a PyTorch model for deployment on Kubernetes. Which THREE tools or formats should they use?

Accepted Answer

Docker. Docker is correct because it is the standard tool for creating container images that package the PyTorch model along with its dependencies, runtime, and environment into a portable artifact. Kubernetes requires container images (typically built with Docker) to deploy and orchestrate workloads, making Docker essential for containerization before deployment.

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MLflow

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Kubeflow

Question 12

An AI system used for autonomous driving is found to have a lower accuracy in detecting pedestrians with darker skin tones. The development team wants to address this ethical issue. Which action is most effective?

Accepted Answer

Augment the training dataset with more images of pedestrians with darker skin. Augmenting the training dataset with more images of pedestrians with darker skin directly addresses the root cause of the bias: underrepresentation in the training data. By providing a more balanced and diverse dataset, the model can learn more robust features for all skin tones, reducing accuracy disparity without altering the algorithm's core logic or introducing arbitrary thresholds.

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Conduct additional testing to measure the disparity

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Replace the object detection algorithm with a different one

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Adjust the model's decision threshold for pedestrian detection

Question 13

A large hospital system deploys an AI triage system for emergency rooms. The system uses patient vitals and symptoms to recommend treatment priority. Six months after deployment, complaints arise that the system frequently underestimates the severity of symptoms for patients from certain ethnic backgrounds. A data scientist runs a bias audit and finds that the model's false negative rate is 20% higher for the minority group. The hospital's AI governance board requires immediate corrective action. The data science team has limited resources and cannot retrain the entire model from scratch. They have access to the training data, which is imbalanced. The model is a gradient boosted tree. Which course of action best addresses the bias while minimizing operational impact?

Accepted Answer

Post-process the model's predictions by adjusting thresholds for the minority group. Post-processing by adjusting decision thresholds for the minority group directly compensates for the higher false negative rate without requiring retraining. Since the team has limited resources and cannot retrain the entire gradient boosted tree model, this approach minimizes operational impact while addressing the bias. The threshold adjustment effectively lowers the probability cutoff for the minority group, making the model more sensitive to their symptoms and reducing underestimation of severity.

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Rebalance the training data using SMOTE and retrain the model

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Use adversarial debiasing during training to remove protected attribute correlations

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Replace the model with a simpler logistic regression model to improve interpretability

Question 14

A developer is building an AI agent that needs to call external tools (e.g., weather API, database) and reason about the results to answer user queries. Which THREE components are essential for implementing this agentic workflow?

Accepted Answer

Planning capability (e.g., step-by-step decomposition). Planning capability enables the agent to decompose complex user queries into manageable sub-tasks, such as retrieving weather data before making a recommendation. This step-by-step reasoning is critical for multi-step workflows where the order of tool calls affects the final answer. Without planning, the agent would lack the structured approach needed to handle dependencies between external tool outputs.

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Fine-tuned domain-specific model

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A vector store for long-term memory

Question 15

A data scientist is tuning a deep learning model. Which TWO hyperparameters directly affect the model's capacity to overfit?

Accepted Answer

Number of layers in the network.. Increasing the number of layers increases the model's depth, which expands its representational capacity and allows it to learn more complex patterns, including noise, thereby directly increasing overfitting risk. Option D is correct because dropout is a regularization technique that randomly drops neurons during training; a low dropout rate (e.g., 0.0) removes this regularization, while a high rate (e.g., 0.5) reduces overfitting by preventing co-adaptation of neurons.

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Batch size.

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Optimizer choice (e.g., SGD vs Adam).

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Learning rate.