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

Question 1

A data science team is building a binary classifier to detect fraudulent transactions. The dataset has only 2% fraud cases. Which data preparation technique is MOST critical to address this imbalance?

Accepted Answer

Apply synthetic minority oversampling (SMOTE) to the training set. With only 2% fraud cases, the dataset is severely imbalanced, which can cause the classifier to be biased toward the majority class (non-fraud) and achieve high accuracy without learning to detect fraud. SMOTE (Synthetic Minority Oversampling Technique) addresses this by generating synthetic examples of the minority class (fraud) in the training set, balancing the class distribution and improving the model's ability to generalize to fraud cases. This is the most critical technique among the options because it directly tackles the class imbalance problem, which is the primary challenge in this scenario.

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Use one-hot encoding on categorical features

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Remove outliers from the transaction amounts

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Normalize all numerical features to have zero mean and unit variance

Question 2

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 3

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. Option D is correct because 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 4

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. Option A is correct because 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 5

An AI engineer needs to select a similarity measure for comparing dense embedding vectors in a vector store for document retrieval. Which two measures are commonly used?

Accepted Answer

Dot product and cosine similarity. Option C is correct because dot product and cosine similarity are the two most commonly used measures for comparing dense embedding vectors in vector stores. Cosine similarity computes the cosine of the angle between vectors, making it length-invariant, while dot product is efficient and directly related to cosine similarity when vectors are normalized. Both are widely supported in vector databases like FAISS and Pinecone for document retrieval tasks.

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Pearson correlation and Spearman rank

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Cosine similarity and Jaccard similarity

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Euclidean distance and Manhattan distance

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.

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Instruction tuning with a smaller dataset

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LoRA (Low-Rank Adaptation) on the base model

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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. Option B is correct because 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. Option A is correct because 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.

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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.

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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.

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Instruction tuning on a large dataset

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Gradient checkpointing

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Full fine-tuning of all parameters