AI0-001 Implementing AI Solutions Practice Test 6 — 15 Questions

Question 1

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?

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

Accepted Answer

Use a cloud-based AI microservice (e.g., Amazon Lex, Azure Bot Service) with a pre-built model. Using a cloud-based AI microservice with a pre-built model is most suitable because it requires minimal AI expertise and infrastructure management. The startup can integrate the service via APIs quickly, leveraging the provider's pre-trained models and scalability. This aligns with their need for rapid integration and limited in-house AI skills.

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Implement an asynchronous processing queue for all user requests

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

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Deploy the model on edge devices for offline inference

Question 2

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 (Reasoning and Acting) pattern interleaves natural-language reasoning traces with tool/API calls, allowing the agent to decide which API to invoke, observe the result, and reason about the next step. This iterative loop is ideal for multi-step tasks like booking flights and hotels, where the sequence depends on intermediate results (e.g., flight availability before hotel dates). It is the best-suited pattern for dynamic, multi-step tool use.

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Implement a simple Retrieval-Augmented Generation (RAG) pipeline

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Fine-tune a model to output API call sequences directly

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Use function calling with a fixed sequence of API calls

Question 3

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. An autoencoder neural network is an unsupervised learning model that learns to compress and reconstruct input data. When trained on normal server metrics, it will accurately reconstruct normal patterns but produce high reconstruction error for anomalous patterns, making it ideal for anomaly detection. This allows the system to alert when metrics deviate significantly from normal.

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Recommendation system model

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Linear regression model

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Image classification model

Question 4

A data scientist is preparing a dataset for training a customer churn prediction model. To prevent train/test leakage, which TWO practices should be followed? (Select TWO)

Accepted Answer

Split the data chronologically (e.g., use data before a certain date for training, after for testing). Option C is correct because splitting data chronologically (e.g., training on records before a cutoff date and testing on records after that date) respects the temporal order of observations and prevents future information from leaking into the training set, which is essential for time-dependent churn prediction. Option E is correct because feature selection must be performed using only the training data; if the test set influences which features are selected, information from the test set leaks into model development and produces overly optimistic performance estimates. Option A is incorrect because removing duplicates only from the test set does not prevent leakage and can distort the test distribution; duplicate handling should be consistent and decided before splitting. Option B is incorrect because random shuffling of the entire dataset before splitting can mix past and future observations, which is especially harmful for temporal churn data and does not by itself prevent leakage. Option D is incorrect because computing normalization statistics on the entire dataset before splitting leaks test-set distribution information into training; normalization statistics must be computed only on the training set and then applied to the test set.

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Remove duplicate records only from the test set to ensure uniqueness

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Shuffle the entire dataset randomly before splitting into train and test sets

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Normalize numerical features using statistics computed on the entire dataset before splitting

Question 5

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 Over-sampling Technique) is the best choice because it generates new, synthetic minority class samples by interpolating between existing minority instances and their nearest neighbors, rather than simply duplicating them. This directly addresses severe class imbalance (95:5) by enriching the minority class without discarding valuable majority data. Unlike random oversampling, SMOTE reduces the risk of overfitting to exact copies and helps the model learn a more generalizable decision boundary.

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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 6

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 with a REST API allow the recommendation engine to be decomposed into independent, scalable services that can be invoked synchronously on each user interaction, returning fresh recommendations in real time. REST provides a lightweight, stateless request/response contract that fits low-latency, per-request inference, and microservices let the recommendation model scale horizontally and be updated independently of the rest of the e-commerce platform. This combination directly satisfies the 'real-time as users browse' requirement without coupling the model to a monolith or introducing batch/async delays.

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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 7

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. An evaluation framework with faithfulness and answer relevancy metrics on a held-out test set is the best approach because it directly measures whether the model's output is grounded in the provided source (faithfulness) and whether it actually addresses the user's question (answer relevancy). These are the standard RAG/LLM evaluation metrics designed to detect hallucinations and off-topic answers systematically. A held-out test set ensures the measurement is repeatable and quantifiable across model versions.

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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 8

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 9

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 10

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 in the LLM allows the model to generate structured API calls (e.g., JSON) based on user requests, enabling the agent to autonomously decide when and which API to call. This pattern is best because it leverages the LLM's reasoning to select appropriate functions and parameters, integrating seamlessly with external systems.

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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 11

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 98% training accuracy versus 72% test accuracy is a classic signature of overfitting — the model has memorized the training data rather than learning generalizable patterns. The appropriate lifecycle response is to address model complexity through regularization (L1/L2, dropout, early stopping) or by reducing the number of parameters/layers. This directly targets the generalization gap rather than the data pipeline or deployment.

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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 12

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 two vectors, focusing only on their direction and ignoring magnitude. It is ideal for comparing dense vector embeddings when the magnitude is not important, as it normalizes the vectors. This makes it the best choice for tasks like text similarity where the length of the document should not affect the similarity score.

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Euclidean distance

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Dot product

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Manhattan distance

Question 13

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. The first step in any AI project lifecycle is problem definition, which involves clearly specifying the business problem, the desired outcomes, and the success criteria. In this case, the team needs to define which fields to extract from invoices and set accuracy targets before proceeding to data preparation, model selection, or acquisition. Without a clear problem definition, subsequent steps lack direction and measurable goals.

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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 14

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 is the best fit because it retrieves relevant passages from the scientific corpus at query time and injects them into the LLM's context, grounding the answer in the actual source documents. This directly satisfies the requirement for high accuracy and traceability to the papers, and it avoids the cost and staleness issues of fine-tuning on a large static corpus. Fine-tuning changes model weights but does not guarantee the model will cite or stay faithful to specific documents.

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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 15

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. Option A is correct because few-shot examples of correct JSON outputs demonstrate the exact structure, key names, and formatting the model should reproduce, which strongly improves adherence to the desired schema. Option C is correct because enabling JSON mode or structured output mode in the model API constrains generation so the response is syntactically valid JSON, directly preventing malformed output. Option D is correct because defining the expected JSON schema in the system prompt gives the model explicit field names, types, and required structure to follow. Option B is not among the marked correct answers; while low temperature can improve determinism, it does not by itself guarantee valid or correctly structured JSON. Option E is not marked correct because chain-of-thought reasoning improves problem-solving but does not enforce JSON syntax or schema compliance.

Answer

Set temperature to 0 to increase determinism

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Use chain-of-thought prompting to reason before output