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Updated: Jul 29, 2026

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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
Passing Score:Approximately 65% (typically 26/40 correct answers; varies slightly by board)
Certificate Validity Period:No expiry for certification (ISTQB certifications are generally lifelong)
Related Certifications:ISTQB Certified Tester Foundation Level (CTFL)
Exam Price:Varies by region (~230–260 EUR typical EU pricing; provider-dependent)
Exam Format:Multiple-choice, Single-answer questions, Scenario-based comprehension questions
Exam Duration:60 (+15 min extension for non-native language candidates)
Available Languages:English, Local language versions depending on member board
Real Exam Qty:40 multiple-choice questions
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
Prompt Engineering for Testing- Prompt design techniques
  • 1. Zero-shot, one-shot, few-shot prompting
    • 2. Structuring prompts for test case generation
      • 3. Prompt chaining and meta prompting
        Organizational Adoption and Governance- Enterprise GenAI adoption
        • 1. Integration into CI/CD pipelines
          • 2. Policy, ethics, and compliance considerations
            • 3. LLMOps and governance models
              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
                  Risk, Quality, and Limitations of GenAI- Risks in GenAI usage
                  • 1. Environmental and energy considerations
                    • 2. Bias and fairness issues
                      • 3. Hallucinations and reasoning errors
                        • 4. Data privacy and security concerns
                          Application of GenAI in Software Testing- Practical use in testing workflows
                          • 1. Regression suite optimization
                            • 2. Defect report analysis and summarization
                              • 3. Test data generation and augmentation
                                • 4. Test case generation using LLMs

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

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

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


                                  2. Who typically defines the system prompt in a testing workflow?

                                  A) End user during normal chat use
                                  B) CI server automatically without human input
                                  C) Product owner in user stories only
                                  D) A tester configuring the assistant


                                  3. 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) Integrating user feedback
                                  C) Iterative prompt modification
                                  D) Output analysis


                                  4. What does an embedding represent in an LLM?

                                  A) A set of test cases for validation
                                  B) Tokens grouped into context windows
                                  C) Numerical vectors capturing semantic relationships
                                  D) Logical rules for reasoning


                                  5. Which statement BEST differentiates an LLM-powered test infrastructure from a traditional chatbot system used in testing?

                                  A) It provides fixed responses from predefined rule sets and scripts
                                  B) It focuses primarily on visual dashboards and user navigation features
                                  C) It dynamically generates test insights using contextual information
                                  D) It produces scripted conversational responses similar to traditional bots


                                  Solutions:

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

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