A team has integrated AI-generated test cases into their pipeline. They notice that the mutation score is high, but the tests are flaky (sometimes pass, sometimes fail). What is the best course of action?

Remove the flaky tests from the suite

Increase the test execution timeout to reduce flakiness

Ignore the flakiness because the mutation score is high

Investigate the flakiness and fix the root cause before relying on the tests

Which AI-based test case generation technique uses a fitness function to guide the search for test inputs that maximize coverage or fault detection?

Machine learning classification

A company wants to integrate AI-generated test cases into their existing CI/CD pipeline. What is the most effective way to do this?

Use the AI tool only in a nightly batch, and never commit the generated tests

Replace the existing test execution stage with the AI generation tool

Have the AI tool directly deploy to production after generating tests

Run the AI generation tool as a separate step before the test execution stage, and commit the generated tests to the repository

A team needs to generate synthetic test data that mimics real user data while ensuring privacy. What is the best approach?

Use a model-based approach to generate data from a state machine

Use a generative adversarial network (GAN) to create synthetic data that statistically resembles the original data

Use the actual production data directly in tests

Use a search-based algorithm to maximize code coverage with random data

A team is testing a system with a large input space. They need to generate test cases that cover both typical and edge cases, but they have limited time. Which approach is most efficient?

Use a generative model to produce a large number of random inputs

Use a search-based algorithm that optimizes for both coverage and diversity of inputs

Use a machine learning classifier to select the most important inputs from historical data

Use a model-based approach to generate test cases from a state machine

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Domain 11Objective 3

Using AI for Test Case Generation CT-AI Practice Questions (Page 2)

Part of the Domain 11: Using AI for Testing domain, which makes up ~11% of our current practice bank. ISTQB does not publish an official question count, but from its 60-minute exam (~25–40 total, ~3–4 in this domain), expect 1–1 from this objective — we provide 24 practice questions to prepare you well beyond it. (estimate)

24questions here

5free pages

5concepts

Questions 6–10

6expert · hard
A team has integrated AI-generated test cases into their pipeline. They notice that the mutation score is high, but the tests are flaky (sometimes pass, sometimes fail). What is the best course of action?
Select an answer first
7foundation · easy
Which AI-based test case generation technique uses a fitness function to guide the search for test inputs that maximize coverage or fault detection?
Select an answer first
8application · medium
A company wants to integrate AI-generated test cases into their existing CI/CD pipeline. What is the most effective way to do this?
Select an answer first
9application · medium
A team needs to generate synthetic test data that mimics real user data while ensuring privacy. What is the best approach?
Select an answer first
10expert · hard
A team is testing a system with a large input space. They need to generate test cases that cover both typical and edge cases, but they have limited time. Which approach is most efficient?
Select an answer first

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