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ISQI CT-GenAI Exam Syllabus Topics:

SectionObjectives
Application of GenAI in Software Testing- Practical use in testing workflows
  • 1. Test data generation and augmentation
    • 2. Test case generation using LLMs
      • 3. Regression suite optimization
        • 4. Defect report analysis and summarization
          Prompt Engineering for Testing- Prompt design techniques
          • 1. Structuring prompts for test case generation
            • 2. Zero-shot, one-shot, few-shot prompting
              • 3. Prompt chaining and meta prompting
                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
                        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
                            Organizational Adoption and Governance- Enterprise GenAI adoption
                            • 1. Policy, ethics, and compliance considerations
                              • 2. LLMOps and governance models
                                • 3. Integration into CI/CD pipelines

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                                  ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 Sample Questions (Q20-Q25):

                                  NEW QUESTION # 20
                                  You are using an LLM to assist in analyzing test execution trends to predict potential risks. Which of the following improvements would BEST enhance the LLM's ability to predict risks and provide actionable alerts?

                                  Answer: D

                                  Explanation:
                                  The effectiveness of an LLM is heavily dependent on the specificity of itsOutput Format. While role definition (Option C) and technical instructions (Option D) are helpful, the most significant "value add" for a test lead is receiving information that is directlyactionable. By expanding the output format to include structuredrisk predictions, severity levels, and recommended actions(Option B), the tester is forcing the LLM to perform a deeper level of analysis. Instead of just "flagging trends," the model must now synthesize the data to determinewhya trend is a risk andwhatthe team should do about it. This aligns with the "Advanced Prompting" section of the CT-GenAI syllabus, which emphasizes using AI for decision support. A structured report that includes a "timeline for intervention" allows the human tester to quickly validate the AI's logic and make informed decisions, transforming the LLM from a simple data summarizer into a strategic predictive tool that actively supports the maintenance of release quality and schedule adherence.


                                  NEW QUESTION # 21
                                  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: B


                                  NEW QUESTION # 22
                                  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?

                                  Answer: B

                                  Explanation:
                                  A/B testing, also known as split testing, is a systematic empirical method used to compare two versions of a prompt (Version A and Version B) to determine which one performs better based on predefined evaluation metrics. In the realm of LLMs, where outputs can be stochastic (probabilistic), A/B testing is essential for mitigating inconsistency. When a team encounters vague or varying results for a user story, simply modifying the prompt iteratively (Option B) may improve the result but does not provide a statistical or objective basis for why one version is superior. Byrunning A/B tests, testers can evaluate prompts against specific KPIs such as accuracy, relevance, format adherence, or the absence of hallucinations. This process involves sending the same input data through both prompt versions multiple times and scoring the outputs. The version that consistently yields the "stronger wording" or more precise testware is then selected as the production standard. This data-driven approach is a cornerstone of prompt engineering in professional environments, ensuring that the most effective linguistic structures are utilized to maximize the model's performance and reliability.


                                  NEW QUESTION # 23
                                  A prompt begins: "You are a senior test manager responsible for risk-based test planning on a payments platform." Which component is this?

                                  Answer: D

                                  Explanation:
                                  In structured prompt engineering, theRolecomponent (also known as a Persona) is used to set the perspective, expertise, and tone of the LLM's response. By assigning the role of a "senior test manager," the tester instructs the model to adopt the specific domain knowledge, vocabulary, and professional standards associated with that position. This technique is highly effective because LLMs are trained on vast datasets containing diverse professional documents; invoking a specific persona helps the model narrow its "latent space" to retrieve information relevant to that specific field. For instance, a senior test manager persona will prioritize risk management, resource allocation, and high-level strategy, whereas a "junior developer" persona might focus more on syntax and local unit tests. WhileContext(Option B) provides the background of the project andInstruction(Option A) defines the specific task to be performed, theRoleserves as the foundation for how those instructions are interpreted. This ensures the generated testware aligns with the expected professional seniority and organizational maturity required for high-stakes environments like a payments platform.


                                  NEW QUESTION # 24
                                  What distinguishes an LLM-powered agent from a basic AI chatbot in test processes?

                                  Answer: D


                                  NEW QUESTION # 25
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