How can generative AI assist in coverage analysis?

By replacing the need for requirement reviews.

By executing all test cases and reporting pass/fail.

By generating the final test summary report.

By automatically identifying requirements that have no corresponding test cases.

Why is it important to critically evaluate outputs from generative AI in test analysis?

Because AI outputs are always too long.

Because AI outputs are always biased.

Because AI cannot understand natural language.

Because AI outputs can be incorrect or incomplete.

A test manager uses a generative AI tool to map test cases to requirements and analyze coverage. The AI reports that all requirements have at least one test case. However, the manager knows that a critical requirement was recently changed. What should the manager do?

Manually verify the coverage for the changed requirement and update the AI's knowledge base.

Ignore the change because the AI's analysis is comprehensive.

Ask the AI to regenerate the coverage report without any additional input.

Trust the AI report because it is based on the latest test case repository.

A test team is considering using a generative AI tool that stores prompts and outputs on a cloud server. The team is concerned about data confidentiality. What is the most important consideration?

The tool's ability to generate test cases quickly.

The tool's cost-effectiveness compared to manual testing.

The tool's ability to integrate with existing test management tools.

The tool's compliance with data protection regulations and the team's security policies.

A team uses generative AI to define test oracles for a feature with clear specifications. The AI suggests an oracle that seems reasonable. What should the team do to ensure the oracle is correct?

Review the oracle against the specifications and refine if necessary.

Ask the AI to generate multiple oracles and use the most complex one.

Use the AI-suggested oracle without further review.

Use the oracle only for positive test cases.

Certified Tester Testing with Generative AI

Domain 2Objective 4

Test Analysis with Generative AI CT-GENAI Practice Questions (Page 4)

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

8concepts

Questions 16–20

16foundation · medium
How can generative AI assist in coverage analysis?
Select an answer first
17foundation · medium
Why is it important to critically evaluate outputs from generative AI in test analysis?
Select an answer first
18application · medium
A test manager uses a generative AI tool to map test cases to requirements and analyze coverage. The AI reports that all requirements have at least one test case. However, the manager knows that a critical requirement was recently changed. What should the manager do?
Select an answer first
19application · medium
A test team is considering using a generative AI tool that stores prompts and outputs on a cloud server. The team is concerned about data confidentiality. What is the most important consideration?
Select an answer first
20application · medium
A team uses generative AI to define test oracles for a feature with clear specifications. The AI suggests an oracle that seems reasonable. What should the team do to ensure the oracle is correct?
Select an answer first

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