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

Question 1

A data scientist is preparing a dataset for a classification model. The dataset has missing values in several features and features with very different scales. Which two data preparation steps should be applied?

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

Accepted Answer

Cleaning and normalization. Cleaning handles missing values (e.g., imputation), and normalization scales features to a similar range, which is important for many ML algorithms.

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Outlier removal and binning

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Feature selection and dimensionality reduction

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Data augmentation and one-hot encoding

Question 2

A team is implementing a RAG system for legal document retrieval. The documents are long (50-100 pages) with clear section headings. They want to ensure that retrieved chunks are semantically coherent and respect document structure. Which chunking strategy is MOST appropriate?

Accepted Answer

Hierarchical chunking: first split by sections, then further split each section into fixed-size chunks with overlap. Hierarchical chunking preserves document structure by first splitting into sections, then further into chunks, maintaining semantic coherence.

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Semantic chunking based on sentence embeddings

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Fixed-size chunking with 256 tokens and no overlap

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Recursive character text splitting with chunk size 1000 and chunk overlap 200

Question 3

A developer is building an AI agent that needs to call external APIs to complete user requests. The agent must decide which API to call based on the user's natural language input. Which technique should the developer use to enable the agent to invoke APIs?

Accepted Answer

Function calling. Function calling (also called tool use) is the native mechanism by which modern LLM APIs let the model emit structured JSON describing which function to invoke and with what arguments. The developer defines a schema for each API, and the model decides at runtime which function to call based on the user's natural language input. This is the standard, purpose-built technique for agentic API invocation.

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Fine-tuning the LLM on API documentation

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Chain-of-thought reasoning

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Few-shot prompting with examples of API calls

Question 4

A machine learning engineer is deploying a real-time anomaly detection system for manufacturing sensor data. The system must process thousands of readings per second with minimal latency. Which deployment architecture is BEST suited?

Accepted Answer

AI microservices with an async processing queue and streaming responses. Real-time anomaly detection on high-throughput sensor streams requires low-latency, scalable, event-driven processing. An AI microservices architecture with an async processing queue and streaming responses decouples ingestion from inference, allowing horizontal scaling of model-serving instances and back-pressure handling. This design keeps latency low while processing thousands of readings per second, unlike batch or monolithic approaches.

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Batch processing using Apache Spark jobs triggered hourly

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Serverless functions deployed on a CDN

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A monolithic web application with a relational database

Question 5

A data scientist is building a binary classification model to predict customer churn. The dataset has 90% non-churn and 10% churn. After training, the model achieves 90% accuracy, but the recall for the churn class is only 20%. Which metric should the team primarily focus on to evaluate the model's effectiveness?

Accepted Answer

Recall for the churn class. With 90% non-churn and 10% churn, a model that predicts 'no churn' for everyone achieves 90% accuracy but 0% churn recall. The current model's 20% churn recall means it misses 80% of actual churners, which is the business-critical failure. Recall for the churn class directly measures how many true churners are caught, making it the primary metric.

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Accuracy

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Area Under the ROC Curve (AUC-ROC)

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Precision for the non-churn class

Question 6

A company has an existing AI chatbot that uses a fine-tuned LLM to answer customer queries. They want to add the ability to retrieve real-time order status from their database. Which integration pattern should they use?

Accepted Answer

Implement function calling so the model can trigger a database query and receive the result. Function calling (tool use) lets the LLM emit a structured call to an external function — here, a database query for order status — and then incorporate the returned result into its response. This is the correct pattern for real-time, dynamic data retrieval because the model does not need retraining and the data stays current.

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Prompt the user to check the order status manually

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Use RAG to retrieve order status from a vector store

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Embed the database query results directly into the model's training data

Question 7

A data science team is preparing a dataset for a supervised learning task. They split the data into training and test sets. The team then normalizes the features using the mean and standard deviation calculated from the entire dataset before splitting. What issue does this introduce?

Accepted Answer

It introduces train/test leakage. Computing normalization statistics (mean and standard deviation) on the entire dataset before splitting means the test set's statistics influence the training data transformation. This is a form of train/test leakage: information from the test set leaks into the training pipeline, producing an optimistically biased estimate of model performance. The correct approach is to fit the scaler on the training set only and apply the same transformation to the test set.

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It improves model generalization

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It causes the model to overfit the training data

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It reduces the variance of the features

Question 8

A developer is building a RAG system and needs to choose a similarity metric for retrieving document chunks. The embedding model they use produces normalized vectors (unit vectors). Which similarity metric is equivalent to cosine similarity in this case?

Accepted Answer

Dot product. For normalized (unit-length) vectors, the dot product equals the cosine of the angle between them, because the magnitudes are both 1. Cosine similarity is defined as the dot product divided by the product of magnitudes; when magnitudes are 1, the denominator is 1, so dot product and cosine similarity are mathematically identical. This makes dot product the correct equivalent metric.

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Jaccard similarity

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Euclidean distance

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Manhattan distance

Question 9

An organization wants to implement an AI system to automatically categorize support tickets into predefined categories. They have a labeled dataset of 10,000 tickets. Which approach is MOST appropriate?

Accepted Answer

Fine-tune a pre-trained text classification model. Fine-tuning a pre-trained text classification model is a standard and effective approach for supervised classification when labeled data is available.

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Use a rule-based system with keyword matching

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Use a prompt-based LLM with few-shot examples

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Train a custom neural network from scratch

Question 10

A team is developing an AI agent to assist users with multi-step tasks such as booking a flight, reserving a hotel, and scheduling a car rental. The agent needs to reason about the order of steps and handle dependencies. Which pattern is BEST suited?

Accepted Answer

ReAct pattern (Reasoning and Acting). The ReAct pattern (Reasoning and Acting) is best suited because it interleaves reasoning traces with tool calls, allowing the agent to dynamically plan and adjust steps based on intermediate results. For multi-step tasks with dependencies (e.g., booking a flight before a hotel), ReAct enables the agent to reason about order, handle failures, and call external APIs step-by-step, which is essential for robust task completion.

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Simple tool use without reasoning

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Using a single prompt with all instructions

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Fine-tuning a model to output all steps at once

Question 11

A data scientist is preparing a dataset for a classification model. The dataset has missing values in several features and features with very different scales. Which two data preparation steps should be applied?

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

Accepted Answer

Feature selection. Feature selection (A) is correct because it removes irrelevant or redundant features from the 100-feature set, directly reducing the dimensionality and mitigating overfitting caused by highly correlated predictors. Dimensionality reduction such as PCA (B) is also correct because it transforms the correlated features into a smaller set of uncorrelated principal components, preserving most variance while reducing overfitting and improving model performance. Data augmentation (C) is not appropriate here since it expands training data (typically for images/text) and does not address feature correlation or dimensionality. Adding more hidden layers (D) increases model complexity and would likely worsen overfitting rather than reduce it. Increasing the learning rate (E) is a training hyperparameter change that does not address correlated features and can even destabilize convergence.

Answer

Data augmentation

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Adding more hidden layers to the neural network

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Increasing the learning rate

Question 12

A company is deploying a generative AI application that produces structured JSON output for downstream processing. They want to ensure the output is consistently valid JSON and matches a specific schema. Which THREE techniques should they use? (Select THREE)

Accepted Answer

Provide few-shot examples of the desired output. Option C is correct because few-shot examples of the desired JSON output condition the model on the exact structure, key names, and formatting expected, which strongly improves schema adherence. Option D is correct because a system prompt that explicitly specifies the expected JSON schema constrains the model's behavior and instructs it to emit only conforming JSON. Option E is correct because JSON mode (structured output) in the API call enforces syntactically valid JSON at the decoding/API layer and, when combined with a schema, validates the response against that schema. Option A is not required and is costly: fine-tuning on JSON outputs can bias style but does not guarantee schema-valid JSON at inference time. Option B is wrong because increasing temperature to 1.5 raises randomness and makes malformed or schema-violating output more likely, not less.

Answer

Fine-tune the model on a dataset of JSON outputs

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Increase the temperature parameter to 1.5

Question 13

A team is building an AI-powered recommendation system for an e-commerce platform. They want to test the system before deployment. Which TWO types of testing are MOST relevant for this AI system? (Select TWO)

Accepted Answer

Evaluation frameworks for model output quality. Option C (Evaluation frameworks for model output quality) is correct because AI/ML systems produce probabilistic outputs that cannot be validated by traditional assertions; frameworks such as accuracy, precision/recall, F1, BLEU, or ROUGE metrics are needed to assess whether the recommendation model generates relevant, high-quality predictions before deployment. Option D (Unit tests for data pipelines) is correct because the recommendation system depends on feature engineering and ETL/data ingestion pipelines; unit tests verify transformations, schema conformance, null handling, and data integrity so that garbage data does not silently degrade model behavior. Option A (Load testing the web server) is not selected because it measures infrastructure throughput and concurrency rather than the AI system's model correctness or data quality. Option B (Integration tests for API calls) is not selected because, while useful generally, it tests service-to-service communication rather than the AI-specific concerns of model output quality and data pipeline correctness. Option E (Regression testing on the UI) is not selected because it validates front-end behavior and visual consistency, which is peripheral to testing the AI model and its data pipeline.

Answer

Load testing the web server

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Integration tests for API calls

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Regression testing on the UI

Question 14

A team is implementing a document intelligence solution to extract key-value pairs from invoices. They plan to use a pre-trained vision-language model with a RAG pipeline that indexes invoice images. Which chunking strategy is BEST suited for invoice documents that have a consistent layout but vary in length?

Accepted Answer

Hierarchical chunking that groups lines into logical sections (header, line items, totals). Invoices typically have sections (header, line items, totals). Hierarchical chunking preserves this structure, enabling retrieval at the section level. Fixed-size may split important fields, semantic chunking is less predictable on structured documents.

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Semantic chunking based on sentence boundaries

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Fixed-size chunking with 512 tokens per chunk

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No chunking; pass the entire invoice as one document per query

Question 15

Which similarity metric is MOST appropriate for comparing dense vector embeddings in a vector store used for document retrieval, when the embeddings are normalized to unit length?

Accepted Answer

Cosine similarity. Cosine similarity measures the angle between two vectors and is the standard metric for comparing dense embeddings in vector stores. When embeddings are normalized to unit length, cosine similarity is mathematically equivalent to the dot product, making it both efficient and semantically meaningful for document retrieval. It focuses on orientation rather than magnitude, which aligns with how embedding models encode semantic similarity.

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Jaccard similarity

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Manhattan distance

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Euclidean distance