What is the primary purpose of a generative AI strategy in software testing?

To guarantee that all test cases are generated automatically without human intervention

To provide a structured approach for aligning AI capabilities with testing goals and organizational objectives

To replace all manual testing activities with automated AI-driven testing

To eliminate the need for test data management by using synthetic data exclusively

What is a key consideration when integrating generative AI into existing testing processes?

Replacing all existing testers with AI

Ensuring compatibility with current test management tools

Using AI only for manual testing activities

Ignoring current testing practices to focus solely on AI

How should generative AI be integrated into existing testing workflows?

By using AI only for regression testing and nothing else

By immediately replacing all manual test cases with AI-generated ones

By gradually introducing AI in specific areas and evaluating impact

By keeping AI separate from the main testing process

A testing team uses a generative AI tool to create test scripts. The tool occasionally produces scripts that contain copyrighted code snippets from open-source projects. The company is concerned about legal liability. What should the team do?

Add a disclaimer to the test scripts stating they are AI-generated.

Implement a review process to check AI-generated scripts for copyrighted content before use.

Stop using the AI tool immediately and revert to manual script creation.

Assume that AI-generated content is original and does not infringe copyright.

A healthcare software company plans to use generative AI to create test data for patient management systems. The data includes personal health information. The legal team requires that all data processing comply with HIPAA and that patient data never be exposed to external AI services. Which governance measure should the company implement?

Obtain patient consent for using their data in AI testing.

Use a public generative AI service but anonymize all patient data before sending it.

Deploy a private or on-premises generative AI model that processes data within the company's controlled environment.

Rely on the AI vendor's compliance certifications to ensure data protection.

Certified Tester Testing with Generative AI

Domain 5Objective 2

Key Aspects of a Generative AI Strategy in Software Testing CT-GENAI Practice Questions (Page 3)

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

29questions here

6free pages

8concepts

Questions 11–15

11foundation · easy
What is the primary purpose of a generative AI strategy in software testing?
Select an answer first
12foundation · easy
What is a key consideration when integrating generative AI into existing testing processes?
Select an answer first
13foundation · easy
How should generative AI be integrated into existing testing workflows?
Select an answer first
14application · medium
A testing team uses a generative AI tool to create test scripts. The tool occasionally produces scripts that contain copyrighted code snippets from open-source projects. The company is concerned about legal liability. What should the team do?
Select an answer first
15application · medium
A healthcare software company plans to use generative AI to create test data for patient management systems. The data includes personal health information. The legal team requires that all data processing comply with HIPAA and that patient data never be exposed to external AI services. Which governance measure should the company implement?
Select an answer first

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