ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 : CT-GenAI

  • Exam Code: CT-GenAI
  • Exam Name: ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0
  • Updated: Aug 22, 2026
  • Q&As: 42 Questions and Answers

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ISQI CT-GenAI Exam Syllabus Topics:

SectionObjectives
Topic 1: Risk, Quality, and Limitations of GenAI- Risks in GenAI usage
  • 1. Hallucinations and reasoning errors
    • 2. Data privacy and security concerns
      • 3. Bias and fairness issues
        • 4. Environmental and energy considerations
          Topic 2: Prompt Engineering for Testing- Prompt design techniques
          • 1. Prompt chaining and meta prompting
            • 2. Structuring prompts for test case generation
              • 3. Zero-shot, one-shot, few-shot prompting
                Topic 3: Application of GenAI in Software Testing- Practical use in testing workflows
                • 1. Test case generation using LLMs
                  • 2. Defect report analysis and summarization
                    • 3. Test data generation and augmentation
                      • 4. Regression suite optimization
                        Topic 4: Organizational Adoption and Governance- Enterprise GenAI adoption
                        • 1. Policy, ethics, and compliance considerations
                          • 2. Integration into CI/CD pipelines
                            • 3. LLMOps and governance models
                              Topic 5: Foundations of Generative AI and LLMs- Introduction to Generative AI in Software Testing
                              • 1. LLM basics, tokenization, context window, multimodal models
                                • 2. Difference between chatbots and LLM-based test tools

                                  ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 Sample Questions:

                                  1. What is a primary compliance concern related to Shadow AI in organizational test environments?

                                  A) Violation of established data handling and regulatory compliance standards
                                  B) Difficulty in aligning project milestones with business outcomes
                                  C) Failure to update system documentation within the test process
                                  D) Automated compliance validation during AI tool deployment


                                  2. When an organization uses an AI chatbot for testing, what is the PRIMARY LLMOps concern?

                                  A) Focusing primarily on user experience improvements and response formatting
                                  B) Maintaining data privacy and minimizing security risks from external services
                                  C) Achieving faster responses by reducing model checkpoints and updates
                                  D) Maximizing scalability by deploying larger cloud-based LLM clusters


                                  3. A prompt section states: "Web checkout module v3.2; focus on coupon application; existing regression suite IDs T-112-T-150; recent defect ID BUG-431." Which component is this?

                                  A) Output format
                                  B) Constraints
                                  C) Input data
                                  D) Instruction


                                  4. How do tester responsibilities MOSTLY evolve when integrating GenAI into test processes?

                                  A) Transitioning from manual execution to complete automation with no human oversight
                                  B) Moving from black-box exploratory testing toward exclusively performing code-based white-box checks
                                  C) Replacing existing test coverage validation with automated summary reports generated by AI
                                  D) Shifting from test execution toward reviewing, refining, and validating AI-generated testware


                                  5. Which option BEST differentiates the three prompting techniques?

                                  A) Chaining = give examples; Few-shot = break tasks; Meta = manual edits only
                                  B) Few-shot = no examples; Chaining = single prompt; Meta = disable iteration
                                  C) Few-shot = examples; Chaining = multi-step prompts; Meta = model helps draft/refine prompts
                                  D) Meta = step decomposition; Chaining = zero-shot only; Few-shot = manual optimization


                                  Solutions:

                                  Question # 1
                                  Answer: A
                                  Question # 2
                                  Answer: B
                                  Question # 3
                                  Answer: C
                                  Question # 4
                                  Answer: D
                                  Question # 5
                                  Answer: C

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