A team uses RAG to generate test data. The LLM sometimes produces data that violates business rules. Which prompt construction technique would best mitigate this?

Use a lower temperature to make the output more deterministic.

Increase the number of retrieved documents.

Include the business rules in the retrieved context and instruct the LLM to follow them strictly.

What is a common challenge in RAG systems for testing, and what is a typical mitigation?

Hallucination; mitigation: provide retrieved context and instruct the LLM to base answers on it

Data privacy; mitigation: store all data in the prompt

Retrieval errors; mitigation: use a larger LLM

Latency; mitigation: increase the number of retrieved documents

What is the main advantage of hybrid retrieval over using only vector search?

It eliminates the need for embeddings

It combines semantic understanding with exact keyword matching

It always returns results faster than vector search

A team evaluates their RAG system for test case generation. They notice that retrieval accuracy is high, but the generated test cases are often irrelevant. What is the most likely cause?

The retrieval technique is keyword-based.

The prompt is not effectively using the retrieved context.

The embedding model is not fine-tuned.

A team is embedding test documentation for RAG. They notice that retrieval quality is poor for questions that require combining information from multiple sections. What is the most likely cause?

The retrieval technique is hybrid.

The embedding model is not fine-tuned.

Chunks are too large, causing information to be diluted.

Chunks are too small, so each chunk lacks the full context needed to answer.

Certified Tester Testing with Generative AI

Domain 4Objective 2

Retrieval-Augmented Generation CT-GENAI Practice Questions (Page 4)

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

6free pages

7concepts

Questions 16–20

16application · medium
A team uses RAG to generate test data. The LLM sometimes produces data that violates business rules. Which prompt construction technique would best mitigate this?
Select an answer first
17foundation · easy
What is a common challenge in RAG systems for testing, and what is a typical mitigation?
Select an answer first
18foundation · easy
What is the main advantage of hybrid retrieval over using only vector search?
Select an answer first
19expert · hard
A team evaluates their RAG system for test case generation. They notice that retrieval accuracy is high, but the generated test cases are often irrelevant. What is the most likely cause?
Select an answer first
20application · medium
A team is embedding test documentation for RAG. They notice that retrieval quality is poor for questions that require combining information from multiple sections. What is the most likely cause?
Select an answer first

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