Which of the following is a key element that a generative AI prompt should include when generating test cases from a test condition?

The expected result for each test case.

The name of the test automation tool.

The date of the test execution.

The number of testers available.

A test team uses a generative AI tool to generate test data for a financial application. The AI produces data that is mostly from a single demographic group, which could introduce bias in testing. What is the most effective way to address this?

Manually review and supplement the data to ensure diversity and representativeness.

Use the data as-is and rely on the AI to correct any issues in future runs.

Accept the data because the AI is unbiased by design.

Discard the AI-generated data and create all test data manually.

A team uses generative AI to generate test cases for an e-commerce checkout process. The AI suggests test cases with valid and invalid payment details, but the team needs to ensure the test cases include realistic input data and expected results. What is the best way to refine the AI's output?

Use the AI's test cases as-is and manually add expected results later.

Ask the AI to generate test cases with only valid data to simplify testing.

Provide the AI with specific examples of realistic data and expected outcomes in the prompt.

Ask the AI to generate only edge cases, as they are the most important.

A tester uses generative AI to identify test conditions from a user story. The AI output includes a condition that is not relevant to the story. What is the most appropriate action?

Ask the AI to generate more conditions to see if the irrelevant one becomes relevant.

Keep the condition because it might be useful later.

Use the condition as a negative test case.

Remove the irrelevant condition and refine the prompt to improve future outputs.

What is a test oracle in the context of software testing?

A report that summarizes test results.

A database that stores test data.

A source of expected results to determine if a test passed or failed.

A tool that executes test cases automatically.

Certified Tester Testing with Generative AI

Domain 2Objective 4

Test Analysis with Generative AI CT-GENAI Practice Questions (Page 2)

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)

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Questions 6–10

6foundation · medium
Which of the following is a key element that a generative AI prompt should include when generating test cases from a test condition?
Select an answer first
7expert · hard
A test team uses a generative AI tool to generate test data for a financial application. The AI produces data that is mostly from a single demographic group, which could introduce bias in testing. What is the most effective way to address this?
Select an answer first
8expert · hard
A team uses generative AI to generate test cases for an e-commerce checkout process. The AI suggests test cases with valid and invalid payment details, but the team needs to ensure the test cases include realistic input data and expected results. What is the best way to refine the AI's output?
Select an answer first
9expert · hard
A tester uses generative AI to identify test conditions from a user story. The AI output includes a condition that is not relevant to the story. What is the most appropriate action?
Select an answer first
10foundation · medium
What is a test oracle in the context of software testing?
Select an answer first

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