AI0-001 Implementing AI Solutions Practice Test 4 — 15 Questions

Question 1

A data scientist is fine-tuning a large language model for a domain-specific task using QLoRA. Which TWO statements correctly describe QLoRA's advantages?

Accepted Answer

It enables fine-tuning on consumer-grade GPUs by reducing memory requirements. QLoRA uses 4-bit quantization to reduce memory, and freezes the base model while training low-rank adapters. It does not reduce inference latency (base model still runs), and does not require more data than full fine-tuning.

Answer

It requires more training data than full fine-tuning to achieve comparable accuracy

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It trains the full model parameters with low precision

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It increases inference speed compared to the base model

Question 2

A team is developing an AI agent that can answer questions by querying a SQL database and a REST API. The agent should decide which tool to call, parse the response, and reason about the next step. Which THREE concepts should be implemented to build this agent?

Accepted Answer

ReAct pattern for iterative reasoning and tool use. The ReAct pattern (Reasoning + Acting) enables iterative tool selection. Function calling allows the LLM to output structured tool calls. Planning agents can decompose a question into subtasks. Chain-of-thought is a reasoning technique but not a full agent framework; content filtering is not needed.

Answer

Content filtering to sanitize database results

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Chain-of-thought prompting without tool integration

Question 3

An organization is deploying an image classification model to detect defects on a production line. Which TWO steps are essential during the model monitoring phase of the AI project lifecycle?

Accepted Answer

Track model performance metrics such as precision and recall on a held-out test set over time. Monitoring should track data drift (changes in input distribution) and model drift (performance degradation). Retraining is an action after monitoring, not a step of monitoring itself. Data preparation is completed before deployment.

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Perform data cleaning and normalization on the production data before inference

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Periodically retrain the model with newly labeled data

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Automatically roll back to the previous model version if a metric drops below a threshold

Question 4

A company wants to build a customer service chatbot that answers questions about their internal policy documents. The documents are updated monthly, and the team cannot afford to retrain a model each time. Which approach is MOST appropriate?

Accepted Answer

Use Retrieval-Augmented Generation (RAG) with the policy documents indexed in a vector store. RAG (Retrieval-Augmented Generation) allows the LLM to retrieve relevant document sections at inference time, so knowledge stays current without retraining. The other options either require expensive retraining for each update or lack document grounding.

Answer

Fine-tune a base LLM on the policy documents monthly

Answer

Train a custom model from scratch on the policy documents each month

Answer

Use a larger foundation model with a longer context window and paste all documents into each prompt

Question 5

A data scientist is preparing a dataset for a binary classification model to detect fraudulent transactions. The dataset has 1% fraud cases (minority class) and 99% non-fraud cases. Which data preparation technique is MOST appropriate to address the class imbalance before training?

Accepted Answer

Apply SMOTE (Synthetic Minority Over-sampling Technique) to the minority class. Synthetic Minority Over-sampling Technique (SMOTE) generates synthetic samples for the minority class, balancing the dataset without losing data. Undersampling would discard valuable majority samples, and oversampling by duplication can cause overfitting. Normalization does not fix class imbalance.

Answer

Duplicate the minority class samples until the class ratio is 50:50

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Normalize all features to a range of 0 to 1

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Random undersample the majority class to match the minority class size

Question 6

An AI team is deploying a fine-tuned LLM for a code generation assistant. They need to ensure the model outputs only syntactically valid JSON for integration with downstream systems. Which prompt engineering technique is MOST effective for enforcing structured output?

Accepted Answer

Enable JSON mode in the API call, specifying the desired JSON schema. JSON mode (or structured output) instructs the model to output valid JSON, often with schema enforcement. Few-shot examples may help but aren't as reliable as a dedicated mode. System prompts set the role but don't enforce syntax. Chain-of-thought improves reasoning but not format.

Answer

Provide a few-shot example of a valid JSON response in the prompt

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Include a system prompt that says 'You are a helpful coding assistant.'

Answer

Use chain-of-thought prompting to have the model reason step-by-step before answering

Question 7

A company is building a document intelligence system that extracts key fields from scanned invoices. They have a labeled dataset of 10,000 invoices but need to decide between a traditional OCR+rule-based pipeline and an AI-based model. Which use case characteristic STRONGLY favors the AI-based approach?

Accepted Answer

Invoice layouts vary significantly between different vendors and often change. When invoice layouts vary widely, AI models (especially computer vision + NLP) generalize better than fixed rules. If layouts were consistent, rules might suffice. The other options either favor traditional approaches or are neutral.

Answer

The system must process invoices in real time with sub-second latency

Answer

The team has limited access to labeled training data

Answer

Invoices have a fixed, standardized layout across all vendors

Question 8

During the evaluation phase of an AI project, the team measures the model's F1 score on a held-out test set. They find the F1 score is 0.92, but when deployed in production, the model performs poorly on new data. What is the MOST likely cause of this discrepancy?

Accepted Answer

Data leakage occurred between the training and test sets during preparation. Data leakage during preparation can cause overly optimistic evaluation scores. If the test set contains information from the training set, the model appears better than it really is. Overfitting is possible but less likely with a proper hold-out. Concept drift occurs over time, not immediately. Poor hyperparameter tuning usually yields lower scores, not inflated ones.

Answer

The production data has a different distribution than the training data (concept drift)

Answer

The model's hyperparameters were not properly tuned

Answer

The model is overfitting to the training data

Question 9

A team is implementing a RAG system for a large legal document repository. They need to chunk the documents for efficient retrieval. The documents contain long sections with subsections, and the team wants to preserve the hierarchical structure. Which chunking strategy is MOST appropriate?

Accepted Answer

Hierarchical chunking that preserves section and subsection boundaries. Hierarchical chunking preserves the document structure by maintaining parent-child relationships, which is crucial for legal documents where context from headings matters. Fixed-size may break logical sections; semantic chunking splits by topic but loses hierarchy; overlapping chunks help continuity but don't preserve structure.

Answer

Overlapping chunking with a 10% token overlap

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Semantic chunking based on topic segmentation

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Fixed-size chunking with 512 tokens per chunk

Question 10

A machine learning engineer is building a recommendation system for an e-commerce platform. The system should suggest products based on user purchase history and browsing behavior. Which model selection is BEST suited for this task?

Accepted Answer

Collaborative filtering model (e.g., matrix factorization). Collaborative filtering models (e.g., matrix factorization) are effective for recommendation tasks using user-item interaction data. Linear regression is for regression, not recommendation. Image classification is unrelated. Random forests can be used but are less common for collaborative filtering.

Answer

Image classification model (e.g., CNN)

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Linear regression

Answer

Random forest classifier

Question 11

A startup wants to add an AI-powered virtual assistant to their mobile app. They have limited in-house AI expertise and need a solution that can be integrated quickly with minimal infrastructure management. Which deployment pattern is MOST suitable?

Accepted Answer

Use a cloud-based AI microservice (e.g., Amazon Lex, Azure Bot Service) with a pre-built model. Using AI microservices from a cloud provider (e.g., AWS, Azure, GCP) allows quick integration, scalability, and minimal management. Training on-premises requires expertise and resources. Edge deployment is complex. Async queues are for batch processing, not real-time assistant.

Answer

Implement an asynchronous processing queue for all user requests

Answer

Train and deploy a custom model on an on-premises server

Answer

Deploy the model on edge devices for offline inference

Question 12

During testing of an AI system that classifies support tickets into categories, the team notices the model frequently misclassifies tickets about a new product feature that was introduced after the model was trained. Which type of testing should the team prioritize to catch this issue?

Accepted Answer

Regression testing with a test set that includes examples of the new feature. The model's misclassification of the new product feature is a classic case of data drift, where the production data distribution differs from the training data. Regression testing with a test set that includes examples of the new feature directly validates whether the model still performs correctly on this unseen category. This is the most targeted approach to catch the regression in classification accuracy caused by the new feature.

Answer

Unit tests for the data pipeline

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Integration tests for API calls

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Evaluation framework for LLM output quality

Question 13

A data science team is fine-tuning a large language model for a domain-specific task using LoRA. They have a limited GPU budget and want to minimize memory usage during training. Which technique should they use?

Accepted Answer

Use QLoRA (Quantized LoRA) with a 4-bit quantized base model. QLoRA (Quantized LoRA) quantizes the base model to 4-bit, drastically reducing memory usage while still applying LoRA adapters. Standard LoRA uses full precision. PEFT is a category, not a specific technique. Full fine-tuning uses the most memory.

Answer

Use LoRA (Low-Rank Adaptation) with the base model in full precision

Answer

Use PEFT (Parameter-Efficient Fine-Tuning) without specifying a specific method

Answer

Full fine-tuning of the entire model

Question 14

An AI developer is building an agent that can book flights and hotels by calling external APIs. The agent needs to decide which API to call and in what order based on user requests. Which pattern is BEST suited for this multi-step reasoning and tool use?

Accepted Answer

Apply the ReAct pattern (Reasoning and Acting). The ReAct pattern (Reasoning + Acting) combines chain-of-thought reasoning with tool use, allowing the agent to plan, call APIs, and incorporate results. Function calling alone doesn't provide multi-step reasoning. Simple RAG is for retrieval, not action. Fine-tuning doesn't give dynamic tool selection.

Answer

Implement a simple Retrieval-Augmented Generation (RAG) pipeline

Answer

Fine-tune a model to output API call sequences directly

Answer

Use function calling with a fixed sequence of API calls

Question 15

A team is deploying an anomaly detection system for real-time monitoring of server metrics. The system should alert when metrics deviate significantly from normal patterns. Which type of AI model is MOST suitable?

Accepted Answer

Autoencoder neural network. Autoencoders learn normal patterns and detect anomalies by high reconstruction error. Linear regression predicts continuous values. Image classifiers are for images. Recommendation systems are for user-item interactions.

Answer

Recommendation system model

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Linear regression model

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Image classification model