A test engineer is using an AI to generate test data for a financial application. The initial output contains data that violates business rules (e.g., negative interest rates). The engineer needs to refine the prompt to correct this. What is the most effective refinement?

Request the output in a different format, such as JSON.

Add a constraint: 'Interest rates must be non-negative and within the range 0% to 20%.'

Ask the AI to 'generate better test data'.

Change the role to 'financial data expert'.

What is the purpose of evaluating the quality of AI-generated testing outputs?

To provide context about the system

To determine if the prompt structure needs adjustment

A test manager needs the AI to generate test cases for a legacy system that has limited documentation. The manager wants the AI to focus on high-risk areas but also wants to avoid overwhelming the team with too many test cases. Which prompt best balances risk coverage and volume?

Generate 30 test cases for the legacy system, prioritized by risk.

Act as a risk-based test analyst. The system has limited documentation. Generate test cases for the highest-risk areas: authentication, payment processing, and data migration. Limit the total to 30 test cases, prioritized by risk. Provide the output as a table with a priority column.

Generate test cases for the highest-risk areas of the legacy system.

Generate test cases for the legacy system.

What is the primary purpose of assigning a specific role, such as 'tester' or 'developer', to a generative AI in a prompt?

To enforce a specific output format

To limit the AI's response length

To provide background information about the system under test

To influence the AI's perspective and expertise, improving output relevance

Certified Tester Testing with Generative AI

Domain 2Objective 1

Structure of Prompts for Generative AI in Software Testing CT-GENAI Practice Questions (Page 3)

Part of the Prompt Engineering for Effective Software Testing domain, which makes up ~30% of our current practice bank. ISTQB does not publish an official question count, but from its 60-minute exam (~25–40 total, ~8–12 in this domain), expect 1–1 from this objective — we provide 26 practice questions to prepare you well beyond it. (estimate)

26questions here

6free pages

10concepts

Questions 11–15

11expert · hard
A test engineer is using an AI to generate test data for a financial application. The initial output contains data that violates business rules (e.g., negative interest rates). The engineer needs to refine the prompt to correct this. What is the most effective refinement?
Select an answer first
12foundation · easy
What is the purpose of evaluating the quality of AI-generated testing outputs?
Select an answer first
13expert · hard
A test manager needs the AI to generate test cases for a legacy system that has limited documentation. The manager wants the AI to focus on high-risk areas but also wants to avoid overwhelming the team with too many test cases. Which prompt best balances risk coverage and volume?
Select an answer first
14foundation · easy
What is the primary purpose of assigning a specific role, such as 'tester' or 'developer', to a generative AI in a prompt?
Select an answer first
15foundation · easy
Which role assignment would be most appropriate for a prompt asking the AI to review test cases for potential gaps in coverage?
Select an answer first

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