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Download ISQI : CT-GenAI Questions & Answers as PDF & Test Software

Updated: Sep 13, 2026

No. of Questions: 42 Questions & Answers with Testing Engine

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Go To CT-GenAI Questions

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Our Online Test Engine & Self Test Software of TestSimulate CT-GenAI actual study materials can simulate the exam scene so that you will have a good command of writing speed and time. Then multiple practices make you perfect while in the real ISQI CT-GenAI exam. The package practice version will not only provide you high-quality CT-GenAI exam preparation materials but also various studying ways.

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CT-GenAI Online Engine

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CT-GenAI Practice Q&A's

CT-GenAI PDF
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ISQI CT-GenAI Exam Overview:

Certification Vendor:ISTQB® / iSQI (International Software Quality Institute) / Member Boards
Exam Name:Certified Tester Testing with Generative AI (CT-GenAI)
Exam Number:CT-GenAI v1.0
Real Exam Qty:40 multiple-choice questions
Exam Duration:60 (+15 min extension for non-native language candidates)
Passing Score:Approximately 65% (typically 26/40 correct answers; varies slightly by board)
Related Certifications:ISTQB Certified Tester Foundation Level (CTFL)
Available Languages:English, Local language versions depending on member board
Exam Format:Multiple-choice, Scenario-based comprehension questions, Single-answer questions
Exam Price:Varies by region (~230–260 EUR typical EU pricing; provider-dependent)
Certificate Validity Period:No expiry for certification (ISTQB certifications are generally lifelong)
Recommended Training:ISTQB Sample Exams & Resources
ISTQB CT-GenAI Syllabus (Official PDF)
Exam Registration:Member Board / Exam Providers (varies by country)
ISTQB Official Certification Portal
Sample Questions:ISQI CT-GenAI Sample Questions
Exam Way:Online proctored or test center (Pearson VUE / ISTQB member boards depending on region)
Pre Condition:ISTQB Certified Tester Foundation Level (CTFL) is required
Official Syllabus URL:https://www.istqb.org/certifications/gen-ai/

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
        Risk, Quality, and Limitations of GenAI- Risks in GenAI usage
        • 1. Hallucinations and reasoning errors
          • 2. Bias and fairness issues
            • 3. Data privacy and security concerns
              • 4. Environmental and energy considerations
                Prompt Engineering for Testing- Prompt design techniques
                • 1. Prompt chaining and meta prompting
                  • 2. Zero-shot, one-shot, few-shot prompting
                    • 3. Structuring prompts for test case generation
                      Application of GenAI in Software Testing- Practical use in testing workflows
                      • 1. Test case generation using LLMs
                        • 2. Test data generation and augmentation
                          • 3. Defect report analysis and summarization
                            • 4. Regression suite optimization
                              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:

                                  Question #1

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

                                  • A. Lowering temperature
                                  • B. Increasing learning rate
                                  • C. Increasing temperature
                                  • D. Using a larger context window
                                  Answer: A

                                  Explanation: Only visible for TestSimulate members. You can sign-up / login (it's free).

                                  Question #2

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

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

                                  Explanation: Only visible for TestSimulate members. You can sign-up / login (it's free).

                                  Question #3

                                  Which AI approach requires feature engineering and structured data preparation?

                                  • A. Deep Learning
                                  • B. Symbolic AI
                                  • C. Generative AI
                                  • D. Classical Machine Learning
                                  Answer: D

                                  Explanation: Only visible for TestSimulate members. You can sign-up / login (it's free).

                                  Question #4

                                  What BEST protects sensitive test data at rest and in transit?

                                  • A. Disable TLS and rely on VPN only
                                  • B. Enforce role-based access controls
                                  • C. Use public file shares with read-only links
                                  • D. Rely on obfuscation instead of encryption
                                  Answer: B

                                  Explanation: Only visible for TestSimulate members. You can sign-up / login (it's free).

                                  Question #5

                                  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. A/B testing of prompts
                                  • B. Output analysis
                                  • C. Iterative prompt modification
                                  • D. Integrating user feedback
                                  Answer: A

                                  Explanation: Only visible for TestSimulate members. You can sign-up / login (it's free).

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