Prepare with ISQI CT-GenAI exam torrent, pass for sure

Updated: Aug 17, 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
Real Exam Qty:40 multiple-choice questions
Exam Format:Multiple-choice, Single-answer questions, Scenario-based comprehension questions
Certificate Validity Period:No expiry for certification (ISTQB certifications are generally lifelong)
Passing Score:Approximately 65% (typically 26/40 correct answers; varies slightly by board)
Related Certifications:ISTQB Certified Tester Foundation Level (CTFL)
Exam Price:Varies by region (~230–260 EUR typical EU pricing; provider-dependent)
Available Languages:English, Local language versions depending on member board
Exam Duration:60 (+15 min extension for non-native language candidates)
Recommended Training:ISTQB Sample Exams & Resources
ISTQB CT-GenAI Syllabus (Official PDF)
Exam Registration:ISTQB Official Certification Portal
Member Board / Exam Providers (varies by country)
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
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. Hallucinations and reasoning errors
        • 2. Data privacy and security concerns
          • 3. Bias and fairness issues
            • 4. Environmental and energy considerations
              Prompt Engineering for Testing- Prompt design techniques
              • 1. Zero-shot, one-shot, few-shot prompting
                • 2. Prompt chaining and meta prompting
                  • 3. Structuring prompts for test case generation
                    Application of GenAI in Software Testing- Practical use in testing workflows
                    • 1. Defect report analysis and summarization
                      • 2. Test case generation using LLMs
                        • 3. Regression suite optimization
                          • 4. Test data generation and augmentation
                            Organizational Adoption and Governance- Enterprise GenAI adoption
                            • 1. Integration into CI/CD pipelines
                              • 2. LLMOps and governance models
                                • 3. Policy, ethics, and compliance considerations

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

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

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


                                  2. You are tasked with applying structured prompting to perform impact analysis on recent code changes. Which of the following improvements would BEST align the prompt with structured prompt engineering best practices for comprehensive impact analysis?

                                  A) Include mapping code changes to affected modules, identifying test cases, prioritizing by risk level and change complexity
                                  B) Specify that the role is a test architect specializing in CI/CD pipelines.
                                  C) Include references to version control systems like Git in the constraints.
                                  D) Add a step to review the change log for syntax errors before analysis.


                                  3. An attacker sends extremely long prompts to overflow context so the model leaks snippets from its training data. Which attack vector is this?

                                  A) Malicious code generation
                                  B) Request manipulation
                                  C) Data exfiltration
                                  D) Data poisoning


                                  4. In the context of software testing, which statements (i-v) about foundation, instruction-tuned, and reasoning LLMs are CORRECT?
                                  i. Foundation LLMs are best suited for broad exploratory ideation when test requirements are underspecified.
                                  ii. Instruction-tuned LLMs are strongest at adhering to fixed test case formats (e.g., Gherkin) from clear prompts.
                                  iii. Reasoning LLMs are strongest at multi-step root-cause analysis across logs, defects, and requirements.
                                  iv. Foundation LLMs are optimal for strict policy compliance and template conformance.
                                  v. Instruction-tuned LLMs can follow stepwise reasoning without any additional training or prompting.

                                  A) ii, iii, iv
                                  B) i, iii, v
                                  C) i, ii, iii (Duplicate entry in original source)
                                  D) i, ii, iii


                                  5. You must use GenAI to perform test analysis on a payments module with finalized requirements: (1) generate test conditions, (2) prioritize by risk, (3) check coverage gaps. Which sequence best applies prompt chaining?

                                  A) Prioritize requirements -> generate conditions -> review defects
                                  B) Generate prioritized conditions in one shot -> verify coverage
                                  C) Generate conditions -> prioritize by risk -> map to requirements to find gaps
                                  D) Detect requirement defects -> generate conditions -> prioritize


                                  Solutions:

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

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