AI0-001 Practice Test 45 — 15 Questions

Question 1

A machine learning model for credit card fraud detection is deployed. The model's precision is 0.95 and recall is 0.60. The business cost of missing a fraud is very high. Which of the following should the team prioritize to reduce the number of false negatives?

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

Accepted Answer

Decrease the classification threshold.. Decreasing the classification threshold makes the model more sensitive, classifying more transactions as fraudulent. This increases recall (reducing false negatives) at the cost of precision. Given the high cost of missing fraud, lowering the threshold is the direct way to capture more true positives, even if it increases false positives.

Answer

Use a different model algorithm.

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Add more features.

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Increase the classification threshold.

Question 2

An organization is deploying a conversational AI that handles sensitive customer data. To prevent data leakage via the LLM, which TWO practices should be implemented? (Choose two.)

Accepted Answer

Audit logging of all AI interactions. Option C is correct because audit logging of all AI interactions creates a tamper-evident record of prompts and responses, enabling detection, investigation, and forensic analysis of any data leakage or misuse involving sensitive customer data. Option D is correct because output filtering inspects the model's generated responses and blocks or redacts sensitive information (e.g., PII, credentials, regulated data) before it reaches the user, directly preventing leakage at the point of egress. Option A is not the best fit because differential privacy protects individuals in the training dataset by adding noise during training, but it does not prevent leakage of sensitive data supplied at inference time. Option B is not the best fit because red teaming is a proactive assurance activity that finds weaknesses but does not itself block data leakage in production. Option E is not the best fit because encrypting model weights at rest protects the model artifact from unauthorized access, not the sensitive customer data that may be exposed through prompts or outputs.

Answer

Applying differential privacy to training data

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Conducting regular red teaming exercises

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Encrypting model weights at rest

Question 3

An AI system used for resume screening is found to consistently rank male candidates higher than female candidates with similar qualifications. The HR director wants to remediate this bias without significantly reducing model accuracy. Which technique should be applied?

Accepted Answer

Apply adversarial debiasing to the model during training.. Adversarial debiasing is the correct technique because it directly addresses bias during training by introducing an adversarial network that attempts to predict the protected attribute (e.g., gender) from the model's predictions. The main model is trained to maximize accuracy while minimizing the adversary's ability to infer the protected attribute, thereby reducing bias without a significant drop in predictive performance. This approach is more effective than simple feature removal or data collection because it actively learns to remove correlations between the protected attribute and the output.

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Use a random selection of candidates to avoid bias.

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Remove the gender feature from the dataset and retrain.

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Collect more training data from underrepresented groups.

Question 4

Refer to the exhibit. A data scientist reviews the MLflow run for a Random Forest model on customer churn data. What is the most likely issue with this model?

Accepted Answer

The model is overfitting because there is a large gap between train and validation accuracy.. A large gap between training accuracy (e.g., 0.99) and validation accuracy (e.g., 0.82) indicates that the Random Forest model has memorized the training data but fails to generalize to unseen validation data. This is the classic symptom of overfitting, where the model captures noise rather than the underlying pattern. In MLflow, comparing train and validation metrics directly reveals this discrepancy.

Answer

The model is underfitting because training accuracy is too high.

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The model is performing well because validation accuracy is above 0.8.

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The model has a data leak because dataset version is v2.

Question 5

An AI security team is conducting a threat model for a new document summarization service. They want to identify threats related to spoofing of the AI's identity. Which STRIDE category should they consider?

Accepted Answer

Spoofing. Spoofing is the STRIDE category that covers threats related to impersonating the AI's identity, such as an attacker pretending to be the AI service to gain unauthorized access or deceive users. The question explicitly asks about spoofing of the AI's identity.

Answer

Repudiation

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Tampering

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Information disclosure

Question 6

A company is training a large language model and wants to reduce its carbon footprint. Which practice is MOST effective for reducing training energy consumption while maintaining model quality?

Accepted Answer

Use mixed-precision training and prune unnecessary parameters. Mixed-precision training (e.g., FP16/BF16 with FP32 master weights) reduces memory bandwidth and compute cost per operation, while pruning removes redundant parameters, lowering FLOPs and energy per training step. Together they cut energy consumption substantially without materially degrading model quality when done carefully. This is the most effective listed practice for reducing training energy while preserving quality.

Answer

Increase the batch size to the maximum the GPU memory allows

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Use a larger model architecture to achieve higher accuracy faster

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Train the model on CPUs instead of GPUs

Question 7

A data scientist needs to select a regression model to predict house prices. The dataset contains many features, some of which are irrelevant. Which TWO algorithms are BEST suited for this scenario, and why? (Select TWO)

Accepted Answer

Lasso regression (L1 regularization). Lasso regression (L1 regularization) is correct because its L1 penalty drives the coefficients of irrelevant features exactly to zero, performing automatic feature selection and yielding a sparse, interpretable model well suited to a dataset with many useless predictors. Random Forest is correct because its ensemble of decorrelated decision trees handles high-dimensional feature spaces robustly, captures non-linear relationships and interactions between features, and provides built-in feature-importance scores that tolerate irrelevant variables without overfitting as easily as a single model. Linear regression is not appropriate because it uses all features with no regularization, so irrelevant predictors inflate variance and degrade generalization. Ridge regression shrinks coefficients toward zero but never eliminates them, so it does not perform feature selection. K-Nearest Neighbors is distance-based and suffers from the curse of dimensionality, making it a poor choice when many features are irrelevant.

Answer

Ridge regression (L2 regularization)

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Linear regression

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K-Nearest Neighbors

Question 8

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. This is a prompt injection attack, where an attacker crafts inputs that cause the AI model to override its original instructions or constraints, leading to unintended behavior such as revealing sensitive data. The best mitigation is input validation and context sanitization, which filters or neutralizes malicious prompt content before it reaches the model, preventing the injection from succeeding.

Answer

Model inversion attack; apply differential privacy during training

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Data poisoning attack; implement strict access controls

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Membership inference attack; add noise to model outputs

Question 9

Which THREE of the following are best practices for preventing overfitting in deep learning models?

Accepted Answer

L2 regularization. L2 regularization (also known as weight decay) adds a penalty term proportional to the square of the weight magnitudes to the loss function. This discourages the model from learning overly complex patterns by forcing weights to remain small, which reduces variance and helps prevent overfitting. It is a standard technique in deep learning frameworks like TensorFlow and PyTorch, where it is implemented via the `kernel_regularizer` or `weight_decay` parameter.

Answer

Increasing the number of layers

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Using a larger batch size

Question 10

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). SHAP (SHapley Additive exPlanations) provides per-feature attribution for individual predictions, making it suitable for explaining why a particular review was classified as negative. Option A is incorrect because a decision tree surrogate model is a global explanation method, not a local explanation for a single instance. Option B is incorrect because PCA is a dimensionality reduction technique, not an explainability method. Option D is incorrect because t-SNE is used for high-dimensional data visualization, not for explaining model predictions.

Answer

Decision tree surrogate model

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Principal component analysis

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t-SNE dimensionality reduction

Question 11

A developer is integrating an LLM API into a customer-facing application. They want to prevent unauthorized third parties from using the API key. Which of the following is the BEST approach?

Accepted Answer

Use environment variables to store the API key and implement least-privilege access controls on the server side. The API key must never be exposed to the client or committed to source control. Storing it in server-side environment variables keeps it out of the codebase and client bundle, and least-privilege access controls ensure that even if the server is compromised, the key's blast radius is limited. The server acts as a proxy between the client and the LLM API, so the key is only ever used in a trusted environment.

Answer

Embed the API key in the client-side JavaScript and rely on CORS policies

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Store the API key in the application's source code and use version control to track changes

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Apply rate limiting to the API endpoint to prevent excessive usage

Question 12

A team is designing an AI agent that needs to interact with external APIs, search the web, and perform multi-step reasoning. Which TWO architectural components are essential for this agentic workflow? (Choose TWO.)

Accepted Answer

ReAct pattern (Reasoning + Acting). The ReAct pattern (Reasoning + Acting) is essential because it interleaves chain-of-thought reasoning steps with actions, allowing the agent to plan, invoke tools, observe results, and revise its plan across multiple steps—exactly what multi-step reasoning with external APIs and web search requires. Tool use / function calling is equally essential because it provides the mechanism for the model to invoke external APIs and web search functions with structured arguments and receive structured results back into the reasoning loop. Together, ReAct supplies the iterative reasoning-and-action control flow while function calling supplies the concrete interface to external systems. Fine-tuning the base model is not required for this workflow, since tool use and reasoning patterns can be implemented via prompting and orchestration without retraining weights. Single-turn response generation is insufficient because the scenario demands multi-step iteration rather than one-shot answers. A static prompt with no iterations also fails, as the agent must dynamically observe tool outputs and loop through reasoning cycles.

Answer

Fine-tuning the base model

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Single-turn response generation

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Static prompt with no iterations

Question 13

A company has an existing AI chatbot that uses a fine-tuned LLM to answer customer queries. They want to add the ability to retrieve real-time order status from their database. Which integration pattern should they use?

Accepted Answer

Implement function calling so the model can trigger a database query and receive the result. Function calling (tool use) lets the LLM emit a structured call to an external function — here, a database query for order status — and then incorporate the returned result into its response. This is the correct pattern for real-time, dynamic data retrieval because the model does not need retraining and the data stays current.

Answer

Prompt the user to check the order status manually

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Use RAG to retrieve order status from a vector store

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Embed the database query results directly into the model's training data

Question 14

Which similarity measure is commonly used in vector search to find the angle between vectors, making it well-suited for high-dimensional embeddings?

Accepted Answer

Cosine similarity. Cosine similarity measures the cosine of the angle between two vectors, making it ideal for high-dimensional embeddings because it focuses on orientation rather than magnitude. It is widely used in vector search and NLP tasks where vector direction encodes semantic meaning.

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Manhattan distance

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Euclidean distance

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Dot product

Question 15

Which component in a RAG system is responsible for converting document chunks into numerical representations that enable similarity search?

Accepted Answer

Embedding model. The embedding model converts text chunks into dense numerical vectors (embeddings) that capture semantic meaning, enabling similarity search in the vector store. It is the component that performs the transformation from raw text to vector representations. Without the embedding model, the vector store would have nothing to index or search.

Answer

Vector store index

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Document chunker

Answer

Large language model (LLM)