Which of the following best describes how generative AI can enhance regression testing?

By replacing the need for any human testers

By automatically generating test cases and test data from existing specifications

By eliminating all software defects

By running tests faster than any human could

A team wants to use generative AI to create regression tests for a new feature. They have a set of requirements and existing tests for similar features. What is the most effective way to generate tests that are both relevant and non-redundant?

Provide the AI with the requirements and existing tests, and ask it to generate tests that cover the new feature without duplicating existing coverage.

Ask the AI to generate tests based on the code changes only, without considering requirements.

Use the AI to generate as many tests as possible, regardless of redundancy.

Ask the AI to generate tests only from the requirements, ignoring existing tests.

A team uses generative AI to maintain regression tests. After a change in the application's error messages, several tests fail because they assert on the old messages. The team wants to update the tests efficiently. What is the best approach?

Manually update each test that fails, using the AI only to identify the failing tests.

Delete the failing tests and generate new ones from scratch.

Change the application's error messages back to the old ones to make the tests pass.

Use the AI to analyze the changes and update the assertions in the affected tests to match the new error messages.

What does 'diversity' in AI-generated test data refer to?

The data is generated by different AI models

The data is stored in multiple databases

The data is formatted in different languages

The data covers a wide range of input values and edge cases

A development team has a large regression suite that takes several hours to run. They want to reduce the feedback time in their CI/CD pipeline by running only the most relevant tests for each code change. Which strategy should they implement?

Increase the number of parallel test runners to run the full suite faster.

Randomly select a percentage of tests to run for each build.

Use generative AI to analyze the code changes and select a subset of regression tests that are most likely to be affected.

Run the full regression suite only on weekends to save time during the week.

Certified Tester Testing with Generative AI

Domain 2Objective 6

Automated Regression Testing with Generative AI CT-GENAI Practice Questions (Page 5)

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 36 practice questions to prepare you well beyond it. (estimate)

36questions here

8free pages

10concepts

Questions 21–25

21foundation · easy
Which of the following best describes how generative AI can enhance regression testing?
Select an answer first
22application · medium
A team wants to use generative AI to create regression tests for a new feature. They have a set of requirements and existing tests for similar features. What is the most effective way to generate tests that are both relevant and non-redundant?
Select an answer first
23application · medium
A team uses generative AI to maintain regression tests. After a change in the application's error messages, several tests fail because they assert on the old messages. The team wants to update the tests efficiently. What is the best approach?
Select an answer first
24foundation · easy
What does 'diversity' in AI-generated test data refer to?
Select an answer first
25application · medium
A development team has a large regression suite that takes several hours to run. They want to reduce the feedback time in their CI/CD pipeline by running only the most relevant tests for each code change. Which strategy should they implement?
Select an answer first

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