AI0-001 Implementing AI Solutions Practice Test 8 — 15 Questions

Question 1

A data scientist is preparing a dataset for a text classification model. To prevent train/test leakage, which THREE practices should they follow?

Accepted Answer

Use time-based splitting for temporal data. Time-based splitting preserves the temporal order of data, which is critical for time-series or temporal text data to prevent the model from learning from future information that would not be available at inference time. This avoids train/test leakage where future data leaks into the training set, artificially inflating model performance.

Answer

Shuffle the entire dataset before splitting to ensure randomness

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Apply feature scaling to the entire dataset before splitting

Question 2

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.

Answer

Fine-tuned domain-specific model

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A vector store for long-term memory

Question 3

An AI system must extract text from scanned invoices and output structured fields (invoice number, date, total amount). Which type of AI application is this?

Accepted Answer

Document intelligence. Document intelligence (D) is the correct answer because it specifically refers to AI systems that extract, classify, and structure data from documents like invoices, receipts, and forms. This application uses optical character recognition (OCR) combined with natural language processing (NLP) to identify and output structured fields such as invoice number, date, and total amount, which is exactly what the question describes.

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Chatbot/virtual assistant

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Code generation

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Image classification/object detection

Question 4

A team is deploying a generative AI model for a real-time customer-facing application. They need to balance cost and latency. Which deployment strategy is MOST suitable?

Accepted Answer

AI microservices with streaming responses and async processing queues. AI microservices with streaming responses and async processing queues decouple inference from the request lifecycle, allowing the system to handle variable loads efficiently while maintaining low latency for real-time interactions. This architecture balances cost by scaling only the necessary components (e.g., GPU-backed inference services) and uses streaming (e.g., Server-Sent Events or WebSockets) to deliver partial results, reducing perceived latency for the customer.

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Monolithic API with serverless functions

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Edge deployment on user devices

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Batch processing with synchronous requests

Question 5

A team is evaluating an LLM-based chatbot that frequently hallucinates when answering questions about internal policies. Which testing approach would MOST effectively quantify this issue?

Accepted Answer

Evaluation frameworks for LLM output quality. Evaluation frameworks for LLM output quality, such as those using metrics like faithfulness, factuality, or ROUGE/BLEU scores, are specifically designed to detect and quantify hallucinations by comparing generated responses against a ground-truth knowledge base. This directly measures the rate at which the chatbot fabricates or misstates internal policy details, providing a quantitative baseline for improvement.

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Integration tests for API calls

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Unit tests for the data pipeline

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Regression testing of model accuracy over time

Question 6

A company wants to fine-tune a 70B-parameter LLM for a specialized domain but has limited GPU memory (e.g., 24 GB VRAM). Which technique allows fine-tuning with minimal memory footprint?

Accepted Answer

QLoRA (Quantized Low-Rank Adaptation). QLoRA (Quantized Low-Rank Adaptation) uses 4-bit quantization and low-rank adapters, reducing memory requirements dramatically while preserving fine-tuning quality.

Answer

Instruction tuning with a smaller dataset

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LoRA (Low-Rank Adaptation) on the base model

Answer

Full fine-tuning with gradient checkpointing

Question 7

A team is building a RAG system with a large repository of technical manuals. They want to ensure that each retrieved chunk is semantically coherent and that related concepts are grouped together. Which chunking strategy is BEST?

Accepted Answer

Semantic chunking using a sentence splitter with topic boundaries. Semantic chunking using a sentence splitter with topic boundaries ensures that each chunk is a self-contained, semantically coherent unit by detecting natural topic shifts (e.g., via embedding similarity or discourse markers). This directly supports the requirement for semantically coherent chunks and grouping of related concepts, unlike methods that ignore content meaning.

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Chunking by page number

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Fixed-size chunking with 512 tokens

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Hierarchical chunking with parent-child relationships

Question 8

An AI application needs to generate structured JSON output from an LLM. The development team wants to ensure the output always conforms to a specific schema. Which prompt engineering technique is MOST suitable?

Accepted Answer

System prompt with JSON schema and a 'respond only with valid JSON' instruction. Providing the JSON schema directly in the system prompt, combined with an explicit instruction to respond only with valid JSON, is the most direct and reliable way to constrain an LLM's output format. This technique leverages the model's instruction-following capability and schema awareness without requiring examples or retraining, ensuring strict adherence to the desired structure.

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Few-shot examples showing correct JSON

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Chain-of-thought prompting

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Fine-tuning the model on JSON datasets

Question 9

A data scientist notices that a model's performance on the training set is excellent, but validation accuracy is poor. The team used the same dataset for feature engineering and model selection. What is the MOST likely cause?

Accepted Answer

Train/test leakage caused by using the same data for feature engineering and model selection. Reusing the same data for feature engineering and model selection creates data leakage from validation set into training decisions, leading to overfitting and poor generalization.

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The dataset is too small for the model complexity

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The learning rate is too high

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The model is overfitting due to high variance

Question 10

A company wants to build a system that automatically tags uploaded images with objects they contain (e.g., 'car', 'tree', 'person'). Which AI application type is this?

Accepted Answer

Image classification/object detection. The task of identifying and labeling objects (e.g., 'car', 'tree', 'person') within an image is a classic use case for image classification combined with object detection. Image classification assigns a single label to the entire image, while object detection localizes and classifies multiple objects within the image, which is exactly what the system requires.

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Recommendation system

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Anomaly detection

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Document intelligence

Question 11

A data science team is developing a churn prediction model. Which TWO data preparation best practices are MOST important to prevent overfitting and ensure generalization?

Accepted Answer

Split data into training and test sets before any preprocessing. Splitting data before any preprocessing prevents leakage from test set into training. Cross-validation provides a more robust estimate of generalization than a single hold-out set.

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Normalize all features using the entire dataset's statistics

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Remove outliers based on the full dataset distribution

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Encode categorical variables with target encoding on the full dataset

Question 12

An organization is deploying an AI agent that uses the ReAct pattern to answer customer queries by calling external APIs. Which THREE components are essential in this agentic workflow?

Accepted Answer

A planning algorithm to decompose tasks. The ReAct pattern interleaves reasoning traces with tool calls (function calling), enabling the agent to plan and execute actions step by step. Tool use and multi-step reasoning are core to the pattern.

Answer

Fine-tuning the LLM on domain-specific data

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A vector store for long-term memory

Question 13

A company is choosing between fine-tuning and RAG for a legal document assistant. Which TWO factors would MOST strongly favor RAG over fine-tuning?

Accepted Answer

The legal documents are updated frequently (weekly). RAG allows dynamic retrieval from a changing document base without retraining, and provides citation sources for transparency — critical in regulated domains like law.

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The model needs to understand complex legal terminology

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The queries require deep reasoning across multiple documents

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The company has limited compute budget for training

Question 14

A team is designing an AI microservice for image classification. Which THREE practices should they implement for effective integration and testing?

Accepted Answer

Use streaming responses for real-time feedback. Unit tests for data pipelines ensure data quality, integration tests verify API communication, and streaming responses improve user experience. These cover the main integration concerns.

Answer

Train the model on the full dataset before deployment

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Deploy the model as a monolithic application to simplify testing

Question 15

An AI engineer is selecting a PEFT technique to fine-tune a large language model. Which TWO are examples of PEFT (Parameter-Efficient Fine-Tuning)?

Accepted Answer

LoRA. LoRA and QLoRA are popular PEFT methods that update only a small number of additional parameters while keeping the base model frozen.

Answer

Instruction tuning on a large dataset

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Gradient checkpointing

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Full fine-tuning of all parameters