What new metric might be added to test reporting when using AI-enabled testing?

Total number of test environments used

Average time to execute a single test case

Number of test cases written by humans

Percentage of AI-generated test cases that passed

A test team has implemented an AI-based test execution tool that automatically retries failed tests to distinguish flaky failures from real defects. The team notices that the tool retries tests too aggressively, causing delays in the pipeline. What is the most appropriate way to evolve the execution workflow?

Move the retry logic to a separate nightly job to avoid pipeline delays.

Increase the number of retries to ensure flaky tests are correctly identified.

Configure the AI tool with a maximum retry count and a time limit for retries.

Disable the retry feature and mark all failures as defects.

How can AI assist in test execution?

By guaranteeing that all tests pass without human intervention

By automatically prioritizing test cases based on risk and change impact

By replacing the need for test environments

By writing the final test report automatically without any human review

Which activity is a key adaptation in test planning when incorporating AI-driven testing?

Reducing the need for test data management because AI generates its own data

Eliminating risk analysis because AI can predict all risks

Defining criteria for evaluating the quality of AI-generated test cases

Removing test environment requirements because AI can simulate everything

A test manager wants to report on the effectiveness of AI-generated test cases. The team has been tracking the number of defects found by AI-generated cases versus manually written cases. What is the most meaningful metric to report?

The total number of defects found by all test cases combined.

The number of AI-generated test cases that were executed.

Defect detection rate per test case for AI-generated versus manually written cases.

The time saved by using AI to generate test cases.

Certified Tester Testing with Generative AI

Domain 5Objective 7

Evolving Test Processes in AI-Enabled Test Organizations CT-GENAI Practice Questions (Page 1)

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 1–5

1foundation · easy
What new metric might be added to test reporting when using AI-enabled testing?
Select an answer first
2application · medium
A test team has implemented an AI-based test execution tool that automatically retries failed tests to distinguish flaky failures from real defects. The team notices that the tool retries tests too aggressively, causing delays in the pipeline. What is the most appropriate way to evolve the execution workflow?
Select an answer first
3foundation · easy
How can AI assist in test execution?
Select an answer first
4foundation · easy
Which activity is a key adaptation in test planning when incorporating AI-driven testing?
Select an answer first
5application · medium
A test manager wants to report on the effectiveness of AI-generated test cases. The team has been tracking the number of defects found by AI-generated cases versus manually written cases. What is the most meaningful metric to report?
Select an answer first

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