ISQI CT-GenAI dumps - in .pdf

CT-GenAI pdf
  • Exam Code: CT-GenAI
  • Exam Name: ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0
  • Updated: Aug 19, 2026
  • Q & A: 42 Questions and Answers
  • PDF Price: $59.99

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  • Exam Code: CT-GenAI
  • Exam Name: ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0
  • Updated: Aug 19, 2026
  • Q & A: 42 Questions and Answers
  • PDF Version + PC Test Engine + Online Test Engine
  • Value Pack Total: $119.98  $79.99
  • Save 50%

ISQI CT-GenAI dumps - Testing Engine

CT-GenAI Testing Engine
  • Exam Code: CT-GenAI
  • Exam Name: ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0
  • Updated: Aug 19, 2026
  • Q & A: 42 Questions and Answers
  • Software Price: $59.99
  • Testing Engine

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ISQI CT-GenAI Exam Syllabus Topics:

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

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

                                  1. Which statement BEST describes vision-language models (VLMs)?

                                  A) VLMs are a superset of multimodal LLMs.
                                  B) VLMs are unrelated to multimodal LLMs and focus only on UI automation.
                                  C) VLMs are a subset of multimodal LLMs integrating visual and textual information.
                                  D) VLMs process audio and video but not images.


                                  2. A team notices vague, inconsistent LLM outputs for the same story for two different prompts. Which technique BEST helps choose the stronger wording among two prompt versions using predefined metrics?

                                  A) Output analysis
                                  B) A/B testing of prompts
                                  C) Iterative prompt modification
                                  D) Integrating user feedback


                                  3. Which statement about data privacy risks in GenAI-assisted testing is INCORRECT?

                                  A) Some GenAI tools may store/process data without explicit consent
                                  B) Strict GDPR compliance eliminates all privacy risk
                                  C) GenAI outputs can accidentally reveal sensitive information present in inputs
                                  D) Using GenAI without regulatory compliance can lead to legal exposure


                                  4. Which setting can reduce variability by narrowing the sampling distribution during inference?

                                  A) Using a larger context window
                                  B) Increasing learning rate
                                  C) Increasing temperature
                                  D) Lowering temperature


                                  5. Which option BEST differentiates the three prompting techniques?

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


                                  Solutions:

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

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