A test organization is scaling generative AI and wants to integrate it into their existing test management tool. What is the most important consideration during this integration?

Requiring all testers to manually copy AI outputs into the test management tool.

Choosing the AI tool that is the most popular on the market.

Ensuring that the AI tool can exchange data with the test management tool in a structured format.

Replacing the test management tool with the AI tool's built-in features.

During the exploration and pilot phase, a team encounters resistance from some testers who fear that generative AI will replace their jobs. What is the most effective way to address this challenge?

Ignore the concerns and proceed with the pilot as planned.

Replace the resistant testers with new hires who are more open to AI.

Communicate that AI will augment testers' work and involve them in the pilot design.

Promise that no one will ever be laid off due to AI.

A test team wants to start using generative AI for test data generation. They have a tight deadline and limited budget, and they want to demonstrate value quickly without disrupting ongoing projects. Which approach best fits the exploration and pilot phase?

Purchase an enterprise-wide license and train all testers at once.

Replace the existing test data management tool entirely with the AI tool.

Select a low-risk, non-critical test suite and run a controlled pilot with a small group of testers.

Immediately integrate the AI tool into the CI/CD pipeline for all regression suites.

Why do organizations typically start with low-risk testing tasks during the exploration phase of generative AI adoption?

To minimize potential negative impact while learning how the AI tool behaves

To immediately demonstrate a high return on investment to stakeholders

To avoid the need for human oversight during the pilot

To comply with regulatory requirements that mandate low-risk testing first

Which practice is most associated with the 'Optimization and Governance' phase?

Conducting a one-time demonstration of a generative AI tool to stakeholders

Creating a feedback loop to refine AI prompts based on test results

Migrating all test data to a cloud-based storage solution

Assigning a single test engineer to experiment with AI on a low-risk task

Certified Tester Testing with Generative AI

Domain 5Objective 4

Phases When Adopting Generative AI in Software Testing CT-GENAI Practice Questions (Page 2)

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

26questions here

6free pages

5concepts

Questions 6–10

6application · medium
A test organization is scaling generative AI and wants to integrate it into their existing test management tool. What is the most important consideration during this integration?
Select an answer first
7application · medium
During the exploration and pilot phase, a team encounters resistance from some testers who fear that generative AI will replace their jobs. What is the most effective way to address this challenge?
Select an answer first
8application · medium
A test team wants to start using generative AI for test data generation. They have a tight deadline and limited budget, and they want to demonstrate value quickly without disrupting ongoing projects. Which approach best fits the exploration and pilot phase?
Select an answer first
9foundation · easy
Why do organizations typically start with low-risk testing tasks during the exploration phase of generative AI adoption?
Select an answer first
10foundation · easy
Which practice is most associated with the 'Optimization and Governance' phase?
Select an answer first

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