AI0-001 Practice Test 12 — 15 Questions

Question 1

During data preparation for a classification model, the data scientist notices that one class has 95% of the samples and the other has only 5%. Which technique is MOST appropriate to address this imbalance?

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

Accepted Answer

Apply SMOTE (Synthetic Minority Over-sampling Technique) to generate synthetic samples for the minority class. Resampling techniques like SMOTE generate synthetic samples for the minority class or undersample the majority class, directly addressing class imbalance.

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Shuffle the data randomly before each training epoch

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Remove the minority class samples entirely

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Use a larger learning rate to force the model to pay attention to the minority class

Question 2

A company uses a third-party LLM API to power its customer support chatbot. To prevent prompt injection attacks, which defense is MOST effective at the application layer?

Accepted Answer

Input validation and sanitization. Input validation and sanitization can strip or escape malicious instructions before they reach the LLM, preventing both direct and indirect prompt injection.

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Differential privacy during training

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Rate limiting API calls

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Output filtering of model responses

Question 3

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

Question 4

Which stage of the AI project lifecycle involves splitting data into training, validation, and test sets?

Accepted Answer

Data preparation. Data preparation includes splitting data, cleaning, normalization, and preventing leakage.

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Model evaluation

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Data acquisition

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Problem definition

Question 5

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 6

A team is evaluating an LLM-based code generation assistant. They want to measure the quality of generated code for correctness, security, and efficiency. Which evaluation framework is BEST suited for this task?

Accepted Answer

Pass@k metric using unit tests from benchmarks like HumanEval. HumanEval and similar frameworks (e.g., MBPP) use unit tests to automatically assess functional correctness of generated code, which is the most objective measure for code generation tasks.

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Human evaluation by a panel of experienced developers

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Perplexity of the model on a code corpus

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BLEU score comparing generated code to reference code

Question 7

A data science team is preparing a dataset for a binary classification model to detect fraudulent transactions. The dataset has 99% legitimate and 1% fraudulent examples. Which TWO techniques should the team apply to improve model performance on the minority class?

Accepted Answer

Use class weights in the loss function. Oversampling the minority class (e.g., SMOTE) and using class weights during training are standard approaches to handle imbalanced data. Undersampling the majority class can also help but is less common here; train/test leakage is a separate issue; normalisation may not be needed.

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Undersample the majority class randomly

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Apply data normalisation (z-score) to all features

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Randomly shuffle the dataset to prevent train/test leakage

Question 8

A developer is implementing a RAG system for legal document review. The documents are long (50-100 pages) with dense sections. They need to chunk the documents in a way that preserves semantic coherence while keeping chunks small enough for effective retrieval. Which chunking strategy is MOST appropriate?

Accepted Answer

Semantic chunking based on paragraph and section boundaries. Semantic chunking splits text at natural boundaries (e.g., paragraphs, sections) while ensuring each chunk is coherent, which is crucial for legal documents where meaning can span multiple sentences.

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Hierarchical chunking with parent-child relationships

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Fixed-size chunking with 512 tokens and no overlap

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Chunking by a fixed number of sentences without considering content

Question 9

A company is building a recommendation system for an e-commerce site. They have historical user-item interaction data. Which approach is most appropriate?

Accepted Answer

Train a collaborative filtering model on user-item interactions. Collaborative filtering uses user-item interactions to recommend items based on patterns from similar users or items, without requiring content features.

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Use a large language model to generate random product suggestions

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Use a pre-trained image classification model to recommend visually similar products

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Deploy a rule-based system that always recommends best-selling items

Question 10

Which chunking strategy for RAG is MOST appropriate when documents have a natural hierarchical structure (e.g., sections, subsections)?

Accepted Answer

Hierarchical chunking that preserves document structure. Hierarchical chunking respects the document structure, preserving context and enabling retrieval at different levels (e.g., section or paragraph).

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Fixed-size chunking with no overlap

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Semantic chunking based on sentence boundaries

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Random chunking with varying sizes

Question 11

A developer is implementing a RAG system and needs to choose a similarity metric for retrieving document chunks. The embedding model produces normalized vectors. Which metric is computationally efficient and equivalent to cosine similarity for normalized vectors?

Accepted Answer

Dot product. For normalized vectors, dot product is equivalent to cosine similarity and is faster to compute.

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Euclidean distance

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Hamming distance

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Manhattan distance

Question 12

A data science team is building a model to detect fraudulent transactions. They have a dataset of 1 million normal transactions and 1,000 fraudulent ones. What is the MOST effective data preparation step to handle this imbalance?

Accepted Answer

Apply SMOTE to generate synthetic fraudulent transactions and randomly undersample normal transactions. Combining oversampling of the minority class (e.g., SMOTE) with undersampling of the majority class is a common and effective approach to balance the dataset.

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Delete all normal transactions until the dataset is balanced

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Duplicate the fraudulent transactions 1,000 times

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Train the model on the original dataset; class imbalance does not affect model performance

Question 13

An AI team is deploying a large language model for a customer-facing application. They need to ensure that the model's output is always in valid JSON format for downstream processing. Which prompt engineering technique should they use?

Accepted Answer

Enable JSON mode in the model's API parameters. Structured output via JSON mode (available in many LLM APIs) constrains the model to output only valid JSON, which is critical for programmatic consumption.

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Use few-shot examples of JSON outputs in the prompt

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Post-process the output with a JSON validator and reject invalid responses

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Add a system prompt that says 'You are a helpful assistant.'

Question 14

Which embedding type is MOST suitable for capturing semantic meaning of text in a RAG pipeline?

Accepted Answer

Dense embeddings from a pre-trained transformer model. Dense embeddings represent semantic meaning in a continuous vector space, ideal for similarity search in RAG.

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Bag-of-words vectors

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TF-IDF vectors

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One-hot encoding

Question 15

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