A test team uses a generative AI model to analyze test logs and flag potential defects. The model frequently flags normal behavior as defects, causing the team to investigate many false positives. What is the best way to improve the model's precision?

Use a different generative AI model that is not trained on test logs.

Increase the sensitivity threshold so the model flags more anomalies.

Ignore the model's flags and rely on manual log review.

Provide the model with more examples of normal behavior in the training data.

What is a typical input for a generative AI system that creates test cases from requirements?

The compiled binary of the application under test.

The test execution logs from previous runs.

User stories or functional specifications written in natural language.

The final production database schema and all its data.

Which metric is commonly used to evaluate the quality of generated test data?

The number of characters in each data record.

The color of the data visualization in the report.

The speed at which the data was generated.

Coverage of the data distribution compared to real production data.

A test team uses a generative AI model to generate test cases for a new feature. The model produces many test cases, but some are invalid or redundant. The team wants to ensure the final test suite is high quality. What is the best approach?

Use a second AI model to review the first model's output and remove duplicates.

Automatically add all AI-generated test cases to the suite without review.

Run the test cases and only keep those that pass.

Have a senior tester review and curate the AI-generated test cases before adding them to the suite.

Which of the following is a common use case for generative AI in software testing?

Guaranteeing that all software defects are found before release.

Replacing all manual testing activities with fully automated AI-driven testing.

Generating synthetic test data that mimics production data patterns.

Eliminating the need for test environments by generating results directly.

Certified Tester Testing with Generative AI

Domain 5Objective 5

Essential Skills and Knowledge for Testing with Generative AI CT-GENAI Practice Questions (Page 4)

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

36questions here

8free pages

10concepts

10 Concepts in This Objective

Fundamentals of Generative AI
Generative AI in Software Testing
Test Data Generation
Test Case and Script Generation
Defect and Anomaly Detection
AI Model Evaluation and Validation
Ethical and Legal Considerations
Integration with Test Tools and Frameworks
Human Oversight and Collaboration
Challenges and Limitations

Questions 16–20

16application · medium
A test team uses a generative AI model to analyze test logs and flag potential defects. The model frequently flags normal behavior as defects, causing the team to investigate many false positives. What is the best way to improve the model's precision?
Select an answer first
17foundation · easy
What is a typical input for a generative AI system that creates test cases from requirements?
Select an answer first
18foundation · easy
Which metric is commonly used to evaluate the quality of generated test data?
Select an answer first
19application · medium
A test team uses a generative AI model to generate test cases for a new feature. The model produces many test cases, but some are invalid or redundant. The team wants to ensure the final test suite is high quality. What is the best approach?
Select an answer first
20foundation · easy
Which of the following is a common use case for generative AI in software testing?
Select an answer first

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