AI0-001 Implementing AI Solutions Practice Test 5 — 15 Questions

Question 1

A data scientist is preparing a dataset for a binary classification model. The dataset has 95% majority class and 5% minority class. Which data preparation technique is BEST to address the class imbalance?

Accepted Answer

SMOTE oversampling of the minority class. SMOTE (Synthetic Minority Oversampling TEchnique) generates synthetic samples for the minority class, balancing the dataset without simply duplicating existing minority instances.

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Min-max normalization of all features

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Random undersampling of the majority class

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Removing all minority class samples

Question 2

A team is building a recommendation system for an e-commerce platform. They need to update recommendations in real-time as users browse. Which integration pattern is MOST suitable?

Accepted Answer

AI microservices with a REST API. AI microservices deployed behind a REST API allow each recommendation request to be handled independently, scaling horizontally to meet real-time demands.

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Streaming responses from a single monolithic model

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Batch processing with nightly updates

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Async processing queue with delayed responses

Question 3

During testing of a customer service chatbot, the team notices that the model sometimes generates plausible-sounding but factually incorrect answers about company policies. Which evaluation approach is BEST to systematically detect and quantify this issue?

Accepted Answer

Evaluation framework with faithfulness and answer relevancy metrics on a held-out test set. A comprehensive LLM output evaluation framework using a ground-truth dataset of question-answer pairs and metrics like faithfulness and answer relevancy can detect hallucination systematically.

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Regression testing comparing old and new model outputs

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Unit tests on the data pipeline

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Integration tests for API calls

Question 4

A team is implementing a RAG system for legal document retrieval. The documents are long and cover multiple topics. Which chunking strategy is MOST appropriate to ensure each chunk contains coherent information?

Accepted Answer

Semantic chunking based on topic boundaries. Semantic chunking based on topic boundaries is the most appropriate strategy because legal documents are long and cover multiple topics. By splitting at natural topic shifts (e.g., clauses, sections, or argument transitions), each chunk preserves coherent meaning, which is critical for accurate retrieval and generation in a RAG system. This approach avoids mixing unrelated content within a single chunk, which would degrade the quality of retrieved context.

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Hierarchical chunking with overlapping windows

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Fixed-size chunking with 512 tokens

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Character-level chunking with no overlap

Question 5

An AI practitioner is fine-tuning a large language model for a domain-specific task using a small labeled dataset (500 examples). They have limited GPU memory. Which technique is MOST suitable?

Accepted Answer

QLoRA (Quantized Low-Rank Adaptation). QLoRA (Quantized Low-Rank Adaptation) is the most suitable technique because it combines 4-bit quantization of the base model with low-rank adapter modules, drastically reducing GPU memory usage while still allowing fine-tuning on a small dataset. This approach preserves the model's pre-trained knowledge and avoids catastrophic forgetting, which is critical when only 500 labeled examples are available.

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Full fine-tuning of all model parameters

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Instruction tuning with the full dataset

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Retrieval-Augmented Generation (RAG) without fine-tuning

Question 6

A developer is building an AI agent that needs to call external APIs (e.g., get weather, send email) based on user requests. Which pattern is BEST for enabling the agent to autonomously decide when to call these APIs?

Accepted Answer

Implement function calling in the LLM to generate structured API calls. Function calling allows the LLM to output structured commands that invoke specific API functions, enabling autonomous tool use in a controlled manner.

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Hard-code the API calls in the agent's logic

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Use a chain-of-thought prompt to reason about the steps

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Use a planning agent with a predefined workflow

Question 7

A team is training a image classification model. They split the dataset into training, validation, and test sets. After training, the model achieves 98% accuracy on the training set but only 72% on the test set. Which step in the AI project lifecycle should the team focus on?

Accepted Answer

Model selection – use regularization or reduce model complexity. The large gap indicates overfitting, which is a model selection/regularization issue. They need to apply techniques like dropout, data augmentation, or reduce model complexity.

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Data acquisition – collect more data

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Deployment – re-deploy with a different serving framework

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Data preparation – check for train/test leakage

Question 8

Which similarity search metric is BEST for comparing dense vector embeddings when the magnitude of the vectors is not important, only the direction?

Accepted Answer

Cosine similarity. Cosine similarity measures the cosine of the angle between vectors, focusing on direction and ignoring magnitude, which is ideal when only direction matters.

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Euclidean distance

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Dot product

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Manhattan distance

Question 9

A company wants to build a code generation tool that helps developers write Python functions. The tool must generate syntactically correct code. Which prompt engineering technique is MOST effective?

Accepted Answer

Few-shot prompting with examples of valid Python functions. Few-shot examples showing valid Python function syntax help the model understand the expected output format and generate correct code.

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Chain-of-thought prompting with step-by-step reasoning

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System prompt instructing the model to output JSON

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Instruction fine-tuning on a large Python corpus

Question 10

An AI system performs anomaly detection on sensor data in a manufacturing plant. The model is deployed and running well. After two months, the plant installs new sensors that produce data with a different distribution. The anomaly detection starts failing with many false positives. Which action should the team take?

Accepted Answer

Retrain the model using data from the new sensors and re-deploy. The model needs monitoring and retraining on data from the new sensors. This falls under model monitoring and lifecycle management in deployment.

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Roll back the model to the previous version and ignore the new sensors

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Increase the anomaly threshold to reduce false positives

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Use an ensemble of the old and a new model without retraining

Question 11

A team is building a document intelligence application that extracts key fields from invoices. They have 10,000 labeled invoices. What is the first step in the AI project lifecycle?

Accepted Answer

Problem definition – specify which fields to extract and accuracy targets. Problem definition must come first to scope the project, define success criteria, and decide what fields to extract before any data work.

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Model selection – choose a pre-trained vision transformer

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Data preparation – clean and normalize the invoice images

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Data acquisition – collect additional invoices from public sources

Question 12

A team is considering whether to fine-tune a base LLM or use RAG for a question-answering system over a large, static corpus of scientific papers. The answer must be highly accurate and grounded in the papers. Which approach is BEST and why?

Accepted Answer

RAG because it retrieves and grounds answers in the source documents. RAG retrieves exact passages and grounds answers in the source, which is critical for accuracy and citation in scientific domains. Fine-tuning may memorize but can hallucinate.

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Fine-tuning because it adapts the model to the scientific domain

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Fine-tuning because it is faster at inference time

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RAG because it does not require any labeled data

Question 13

A team is implementing a RAG system. They are designing the document loading and chunking strategy. Which TWO techniques are commonly used for chunking documents? (Select two.)

Accepted Answer

Fixed-size chunking with a token limit. Fixed-size chunking (based on token count) and semantic chunking (based on natural boundaries) are both standard approaches. Hierarchical chunking is less common, and character-level is rarely used. Overlap is a parameter, not a chunking strategy.

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Frequency-based chunking by term occurrence

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Character-level chunking with no overlap

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Hierarchical chunking using document structure

Question 14

A company is deploying an LLM-based chatbot that must output responses in a structured JSON format for downstream processing. Which THREE prompt engineering techniques should the team use to ensure the output is valid and correctly structured? (Select three.)

Accepted Answer

Include few-shot examples of correct JSON outputs. A system prompt defining JSON structure, few-shot examples of valid JSON, and JSON mode in the model API all help produce valid structured output. Chain-of-thought and temperature adjustment do not directly enforce JSON format.

Answer

Set temperature to 0 to increase determinism

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Use chain-of-thought prompting to reason before output

Question 15

An AI team is evaluating whether to use AI for a customer segmentation task. They have a dataset of customer demographics and purchase history. Which TWO conditions would make AI a better choice than a traditional rule-based approach? (Select two.)

Accepted Answer

The data contains complex, non-linear patterns that are not easily captured by rules. Option B is correct because AI techniques like neural networks or gradient-boosted trees excel at capturing complex, non-linear interactions in high-dimensional data (e.g., purchase sequences combined with demographics) that rule-based systems cannot express without an explosion of brittle, hand-crafted conditions. This makes AI the better choice when the underlying patterns are not linearly separable or easily codified as if-then logic.

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The segmentation criteria are well-understood and can be expressed in simple if-then rules

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The segmentation must be fully explainable to regulators

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The team has no access to labeled data