A test team has adopted a generative AI tool that automatically executes regression suites and triages failures by clustering similar stack traces. The team notices that the tool occasionally misclassifies a new defect as a known flaky test, causing the defect to be ignored. The test lead wants to evolve the execution workflow to reduce this risk. What is the most effective process adaptation?

Disable the AI triage feature and return to manual triage of all failures to ensure accuracy.

Increase the similarity threshold for clustering so that fewer failures are grouped together, reducing false flaky classifications.

Add a rule that any failure not seen in the previous two runs is automatically treated as a new defect.

Require human review of all AI-triage decisions before any failure is marked as flaky or deferred.

A test team uses a generative AI tool to create test cases. During a review, the team finds that the AI has generated a test case that includes a hardcoded password from a previous project. What is the most appropriate immediate action to mitigate this AI-specific risk?

Add a security review step for all AI-generated test cases to detect and remove sensitive data.

Delete the specific test case and continue using the AI tool without changes.

Add a prompt instruction to the AI tool to never include passwords in generated test cases.

Retrain the AI model with a dataset that excludes sensitive information.

What is a key consideration when managing test data for AI-based testing?

Reducing the volume of test data to save storage

Eliminating the need for test data by using synthetic data exclusively

Using only production data without masking

Ensuring the test data is representative and unbiased

Which practice supports ethical AI use in test processes?

Using AI to make all testing decisions without human input

Using biased data to train AI models for better performance

Documenting how AI is used and its limitations

Hiding AI failures to avoid negative reports

A team uses generative AI to create boundary value test cases from a requirements document. The AI generates a large number of cases, but the team is concerned that some may be invalid because the requirements are ambiguous. What is the most effective way to integrate AI into test design while managing this risk?

Add all AI-generated cases to the suite and let execution results determine their validity.

Ask the AI to generate fewer cases to reduce the chance of invalid ones.

Have a business analyst review the AI-generated cases against the original requirements before they are added to the suite.

Use the AI-generated cases only for smoke testing, not for full regression.

Certified Tester Testing with Generative AI

Domain 5Objective 7

Evolving Test Processes in AI-Enabled Test Organizations CT-GENAI Practice Questions (Page 6)

Part of the Deploying and Integrating Generative AI in Test organizations domain, which makes up ~23% of our current practice bank. ISTQB does not publish an official question count, but from its 60-minute exam (~25–40 total, ~6–9 in this domain), expect 1–1 from this objective — we provide 34 practice questions to prepare you well beyond it. (estimate)

34questions here

7free pages

8concepts

Questions 26–30

26application · medium
A test team has adopted a generative AI tool that automatically executes regression suites and triages failures by clustering similar stack traces. The team notices that the tool occasionally misclassifies a new defect as a known flaky test, causing the defect to be ignored. The test lead wants to evolve the execution workflow to reduce this risk. What is the most effective process adaptation?
Select an answer first
27application · medium
A test team uses a generative AI tool to create test cases. During a review, the team finds that the AI has generated a test case that includes a hardcoded password from a previous project. What is the most appropriate immediate action to mitigate this AI-specific risk?
Select an answer first
28foundation · easy
What is a key consideration when managing test data for AI-based testing?
Select an answer first
29foundation · easy
Which practice supports ethical AI use in test processes?
Select an answer first
30application · medium
A team uses generative AI to create boundary value test cases from a requirements document. The AI generates a large number of cases, but the team is concerned that some may be invalid because the requirements are ambiguous. What is the most effective way to integrate AI into test design while managing this risk?
Select an answer first

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Evolving Test Processes in AI-Enabled Test Organizations CT-GENAI Practice Questions (Page 6)

Questions 26–30

A test team uses a generative AI tool to create test cases. During a review, the team finds that the AI has generated a test case that includes a hardcoded password from a previous project. What is the most appropriate immediate action to mitigate this AI-specific risk?

What is a key consideration when managing test data for AI-based testing?

Which practice supports ethical AI use in test processes?

Keep studying — Deploying and Integrating Generative AI in Test organizations