A team is using generative AI to create test oracles for a complex system. They notice that the AI sometimes produces incorrect expected outputs, leading to false failures. What is the best mitigation strategy?

Ignore the false failures and focus only on the tests that pass.

Use the AI-generated oracles only for non-critical tests.

Review and validate AI-generated oracles with domain experts before using them in automated tests.

Increase the number of test cases generated by the AI to overwhelm the false failures.

A team has used generative AI to create a large number of regression test cases. Before adopting them, they want to evaluate the quality of these tests. Which metric is most important to assess the effectiveness of the AI-generated tests?

The number of test cases that fail on the first run.

The number of test cases generated by the AI.

Code coverage percentage achieved by the AI-generated tests.

The time taken to generate the tests.

What is a key benefit of using generative AI in regression testing?

It can replace the need for version control

It can reduce the time and effort needed to create and maintain regression test suites

It can guarantee that all defects are found

It can automatically update test oracles when expected outcomes change

A team is automating regression tests for a REST API. They want to use generative AI to automatically verify that responses are correct without manually writing assertions for every endpoint. Which approach is most effective?

Use the AI to generate random responses and compare them to the actual API responses.

Train the AI on historical API responses and use it to infer expected outputs for new test cases.

Ask the AI to write assertions based on the API documentation, but only for the most critical endpoints.

Use the AI to generate test data, but rely on manual verification of the responses.

What is a benefit of integrating generative AI-based regression testing into CI/CD?

It can dynamically generate and update tests as part of the pipeline

It guarantees that no defects are released to production

It reduces the need for version control

It eliminates the need for code reviews

Certified Tester Testing with Generative AI

Domain 2Objective 6

Automated Regression Testing with Generative AI 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 36 practice questions to prepare you well beyond it. (estimate)

36questions here

8free pages

10concepts

Questions 11–15

11application · medium
A team is using generative AI to create test oracles for a complex system. They notice that the AI sometimes produces incorrect expected outputs, leading to false failures. What is the best mitigation strategy?
Select an answer first
12application · medium
A team has used generative AI to create a large number of regression test cases. Before adopting them, they want to evaluate the quality of these tests. Which metric is most important to assess the effectiveness of the AI-generated tests?
Select an answer first
13foundation · easy
What is a key benefit of using generative AI in regression testing?
Select an answer first
14application · medium
A team is automating regression tests for a REST API. They want to use generative AI to automatically verify that responses are correct without manually writing assertions for every endpoint. Which approach is most effective?
Select an answer first
15foundation · easy
What is a benefit of integrating generative AI-based regression testing into CI/CD?
Select an answer first

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