What is the primary purpose of Retrieval-Augmented Generation (RAG) in an LLM-based system?

To permanently store all training data inside the model weights

To replace the LLM with a more efficient search engine

To increase the model's parameter count for better performance

To enable the LLM to access and use external knowledge during generation

A team is building a RAG system for test documentation. They have a mix of short test steps and long test summaries. They want to optimize retrieval quality. Which chunking strategy is most effective?

Use a fixed chunk size of 500 tokens for all documents.

Use a single chunk for each document, regardless of length.

Use different chunk sizes for different document types, e.g., smaller chunks for test steps and larger for summaries.

A QA team uses an LLM to generate test cases for a new feature. The LLM often produces tests based on outdated requirements. They decide to implement a RAG system. Which architecture component is most directly responsible for ensuring the LLM uses the latest requirements?

The prompt template, which instructs the LLM to be thorough

The embedding model, which converts requirements into vectors for similarity search

The retriever, which fetches relevant requirement documents from a vector database

The generator, which synthesizes the final test cases from its internal knowledge

Certified Tester Testing with Generative AI

Domain 4Objective 2

Retrieval-Augmented Generation CT-GENAI Practice Questions (Page 3)

Part of the LLM-Powered Test Infrastructure for Software Testing domain, which makes up ~18% of our current practice bank. ISTQB does not publish an official question count, but from its 60-minute exam (~25–40 total, ~5–7 in this domain), expect 1–1 from this objective — we provide 28 practice questions to prepare you well beyond it. (estimate)

28questions here

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Questions 11–15

11foundation · easy
What is the primary purpose of Retrieval-Augmented Generation (RAG) in an LLM-based system?
Select an answer first
12expert · hard
A team is building a RAG system for test documentation. They have a mix of short test steps and long test summaries. They want to optimize retrieval quality. Which chunking strategy is most effective?
Select an answer first
13application · medium
A QA team uses an LLM to generate test cases for a new feature. The LLM often produces tests based on outdated requirements. They decide to implement a RAG system. Which architecture component is most directly responsible for ensuring the LLM uses the latest requirements?
Select an answer first
14foundation · easy
Which two components are the core parts of a Retrieval-Augmented Generation (RAG) architecture?
Select an answer first
15application · medium
A team is building a RAG system to retrieve test cases from a large repository. They want to ensure that test cases with similar steps but different names are still retrieved. Which retrieval technique is best suited?
Select an answer first

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Retrieval-Augmented Generation CT-GENAI Practice Questions (Page 3)

Questions 11–15

What is the primary purpose of Retrieval-Augmented Generation (RAG) in an LLM-based system?

A team is building a RAG system for test documentation. They have a mix of short test steps and long test summaries. They want to optimize retrieval quality. Which chunking strategy is most effective?

A QA team uses an LLM to generate test cases for a new feature. The LLM often produces tests based on outdated requirements. They decide to implement a RAG system. Which architecture component is most directly responsible for ensuring the LLM uses the latest requirements?

Which two components are the core parts of a Retrieval-Augmented Generation (RAG) architecture?

A team is building a RAG system to retrieve test cases from a large repository. They want to ensure that test cases with similar steps but different names are still retrieved. Which retrieval technique is best suited?

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