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ISQI CT-GenAI Exam Syllabus Topics:

SectionObjectives
Topic 1: Risk, Quality, and Limitations of GenAI- Risks in GenAI usage
  • 1. Bias and fairness issues
    • 2. Data privacy and security concerns
      • 3. Environmental and energy considerations
        • 4. Hallucinations and reasoning errors
          Topic 2: 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
              Topic 3: Application of GenAI in Software Testing- Practical use in testing workflows
              • 1. Defect report analysis and summarization
                • 2. Test data generation and augmentation
                  • 3. Regression suite optimization
                    • 4. Test case generation using LLMs
                      Topic 4: Prompt Engineering for Testing- Prompt design techniques
                      • 1. Prompt chaining and meta prompting
                        • 2. Structuring prompts for test case generation
                          • 3. Zero-shot, one-shot, few-shot prompting
                            Topic 5: Organizational Adoption and Governance- Enterprise GenAI adoption
                            • 1. LLMOps and governance models
                              • 2. Policy, ethics, and compliance considerations
                                • 3. Integration into CI/CD pipelines

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                                  ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 Sample Questions (Q23-Q28):

                                  NEW QUESTION # 23
                                  An LLM prioritizes tests using likelihood X impact but ranks a trivial tooltip change above a payment failure.
                                  What defect does this MOST LIKELY show?

                                  Answer: C

                                  Explanation:
                                  This scenario describes a failure in the model's ability to apply logical weight to specific domain concepts, specifically in the context of Risk-Based Testing (RBT). When an LLM ranks a low-impact UI element (a tooltip) higher than a critical functional failure (payment processing), it demonstrates a "Reasoning error in risk calculation logic." While LLMs can follow formulas like $Risk = Likelihood \times Impact$, they may lack the deep semantic understanding of "Impact" within a specific business domain unless explicitly guided.
                                  This is not necessarily a hallucination (Option C), as the model isn't necessarily inventing facts, but rather misapplying the logic of prioritization. It is also distinct from dataset bias (Option D), which would involve a systematic skewing across all outputs. In professional testing, this type of error highlights the necessity of
                                  "human-in-the-loop" verification. Testers must review AI-generated prioritizations to ensure that the logical deductions align with the actual business risk and technical criticality of the features being tested.


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

                                  Answer: D


                                  NEW QUESTION # 25
                                  Which statement BEST contrasts interaction style and scope?

                                  Answer: C

                                  Explanation:
                                  It is important to distinguish between a general-purposeChatbotand a specializedLLM applicationwithin a testing framework. A chatbot is primarily designed for multi-turn, conversational interactions where the user can ask questions and receive answers in a natural language format. While useful for general queries, it often lacks the specialized workflow integration needed for rigorous testing. Conversely,LLM applications(or
                                  "LLM-powered tools") are built with a specific "scope" in mind, such as automated test generation, code analysis, or requirement mapping. These applications often use the LLM as an underlying engine but surround it with specific UI components, data connectors (like RAG), and fixed task-oriented prompts to achieve a defined testing outcome. While chatbots are "free-form," LLM apps are "capability-driven." This distinction is key for organizations defining a GenAI strategy; simply providing a chatbot to testers is rarely sufficient.
                                  Instead, organizations should develop or adopt LLM applications that integrate directly into the CI/CD pipeline and provide structured, actionable test artifacts that support defined quality engineering tasks.


                                  NEW QUESTION # 26
                                  Who typically defines the system prompt in a testing workflow?

                                  Answer: D

                                  Explanation:
                                  In professional Generative AI applications, thesystem prompt(sometimes called the system message) is the foundational set of instructions that defines the AI's persona, boundaries, and overall behavior. In a testing workflow, this is typically defined by atester or test engineerwho is configuring the AI assistant for a specific project. Unlike the user prompt, which changes with every interaction, the system prompt remains relatively static and acts as a "guardrail" to ensure the model stays in its role (e.g., "You are an expert in ISO
                                  26262 automotive testing standards"). By defining the system prompt, the tester ensures that the model consistently uses specific terminology, adheres to data privacy constraints, and formats its output according to the team's requirements. While end users (Option B) provide the task-specific input, they do not usually have the permissions or technical need to alter the underlying system-level instructions. Similarly, while CI servers (Option C) might trigger the prompt, they do not "define" the human-centric logic contained within it.
                                  Properly crafting the system prompt is a core part of setting up an AI-augmented test environment.


                                  NEW QUESTION # 27
                                  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: A


                                  NEW QUESTION # 28
                                  ......

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