2026 ISQI CT-GenAI: Pass-Sure ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 Exam Sample Questions

Beyond knowing the answer, and actually understanding the CT-GenAI test questions puts you one step ahead of the test. Completely understanding a concept and reasoning behind how something works, makes your task second nature. Your CT-GenAI test questions will melt in your hands if you know the logic behind the concepts. Any legitimate CT-GenAI Test Questions should enforce this style of learning - but you will be hard pressed to find more than a CT-GenAI test questions anywhere other than VCEDumps.

ISQI CT-GenAI Exam Syllabus Topics:

SectionObjectives
Organizational Adoption and Governance- Enterprise GenAI adoption
  • 1. Policy, ethics, and compliance considerations
    • 2. Integration into CI/CD pipelines
      • 3. LLMOps and governance models
        Risk, Quality, and Limitations of GenAI- Risks in GenAI usage
        • 1. Bias and fairness issues
          • 2. Hallucinations and reasoning errors
            • 3. Data privacy and security concerns
              • 4. Environmental and energy considerations
                Application of GenAI in Software Testing- Practical use in testing workflows
                • 1. Test case generation using LLMs
                  • 2. Regression suite optimization
                    • 3. Test data generation and augmentation
                      • 4. Defect report analysis and summarization
                        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
                            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

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                                  ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 Sample Questions (Q11-Q16):

                                  NEW QUESTION # 11
                                  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?

                                  Answer: C


                                  NEW QUESTION # 12
                                  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?

                                  Answer: C

                                  Explanation:
                                  The most effective way to improve an LLM's performance on complex tasks likeimpact analysisis to provide a detailed, multi-stepInstructionorChain-of-Thoughtstructure. Option D is the best improvement because it breaks the "impact analysis" task into logical sub-tasks: mapping changes to modules, identifying related test cases, and prioritizing them based on risk and complexity. This structured approach guides the LLM through the "reasoning" steps a human expert would take, significantly reducing the likelihood of a superficial or incorrect analysis. While specifying a specialized role (Option B) or adding technical references (Option A) can help set the tone, they do not provide the model with the logical framework required to execute the task accurately. By explicitly defining theprocessthe LLM should follow, the tester ensures that the model evaluates the "depth" of the change rather than just listing files. This results in a more robust and actionable regression test suite, which is the primary goal of impact analysis in a modern software development lifecycle.


                                  NEW QUESTION # 13
                                  Which standard specifies requirements for managing AI systems within an organization, supporting consistent GenAI use in testing?

                                  Answer: A

                                  Explanation:
                                  ISO/IEC 42001:2023is the international standard for an AI Management System (AIMS). It is designed to help organizations develop, provide, or use AI systems responsibly by providing a certifiable framework of requirements and controls. In a software testing context, this standard is vital for establishing governance, ensuring that GenAI tools are used consistently and ethically across the lifecycle.NIST AI RMF 1.0(Option B) is a highly respected framework, but it is a set of voluntary guidelines for managing risk, not a
                                  "requirement standard" for a management system.ISO/IEC 23053:2022(Option C) provides a general framework for AI using machine learning but lacks the comprehensive "management system" scope found in
                                  42001. Finally, theEU AI Act(Option D) is a regulation (law), not a technical standard. For a test organization looking to align its GenAI strategy with international best practices and achieve formal certification, ISO/IEC
                                  42001 is the definitive standard to follow, as it covers the organizational processes, data handling, and risk management necessary for high-quality AI operations.


                                  NEW QUESTION # 14
                                  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.

                                  Answer: D

                                  Explanation:
                                  Understanding the hierarchy of LLM types is vital for selecting the right tool for specific testing tasks.
                                  Foundation LLMsare trained on massive datasets to predict the next token; they excel at broad, creative
                                  "ideation" (Statement i) but often struggle with following specific instructions or constraints (making Statement iv incorrect).Instruction-tuned LLMshave undergone additional training (Fine-tuning) to follow explicit commands and templates. They are highly effective at structured tasks like converting requirements into Gherkin feature files (Statement ii).Reasoning LLMs(or those utilizing specialized prompting like Chain- of-Thought) are designed to handle complex, multi-stage logic. This makes them the superior choice for diagnostic tasks like root-cause analysis, where the model must synthesize information across logs and requirements to find a defect's origin (Statement iii). Statement v is incorrect because while instruction-tuned models are capable, complex "stepwise reasoning" usually requires specific prompting techniques or the inherent logic of specialized reasoning models. Therefore, the combination of i, ii, and iii represents the correct alignment of model capability to testing functionality.


                                  NEW QUESTION # 15
                                  How do tester responsibilities MOSTLY evolve when integrating GenAI into test processes?

                                  Answer: D

                                  Explanation:
                                  As Generative AI is integrated into the testing lifecycle, the role of the human tester undergoes a significant shift from "author" to "orchestrator and reviewer." In traditional testing, a significant portion of a tester's time is spent manually drafting test cases, scripts, and documentation. With GenAI, these artifacts can be generated in seconds. Consequently, the tester's responsibility shifts towardreviewing, refining, and validatingthe AI- generated testware to ensure accuracy, relevance, and compliance with project goals. This "Human-in-the- Loop" (HITL) approach is critical because LLMs are prone to hallucinations and may lack the deep domain context of a human expert. Testers must apply their critical thinking to verify that the AI-generated scripts actually cover the necessary edge cases and do not contain logical errors. This evolution does not mean the end of human oversight (Option B) or a move exclusively to white-box testing (Option C). Instead, it elevates the tester to a higher-level analytical role, focusing on quality strategy and the final verification of AI outputs rather than the repetitive task of initial content creation.


                                  NEW QUESTION # 16
                                  ......

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