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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. Zero-shot, one-shot, few-shot prompting
            • 2. Structuring prompts for test case generation
              • 3. Prompt chaining and meta prompting
                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
                          Risk, Quality, and Limitations of GenAI- Risks in GenAI usage
                          • 1. Data privacy and security concerns
                            • 2. Hallucinations and reasoning errors
                              • 3. Environmental and energy considerations
                                • 4. Bias and fairness issues

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

                                  Question #1

                                  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 references to version control systems like Git in the constraints.
                                  • B. Add a step to review the change log for syntax errors before analysis.
                                  • C. Include mapping code changes to affected modules, identifying test cases, prioritizing by risk level and change complexity
                                  • D. Specify that the role is a test architect specializing in CI/CD pipelines.
                                  Answer: C
                                  Question #2

                                  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?

                                  • A. Emphasize constraints that focus on deviations that could impact release timelines or quality gates.
                                  • B. Expand the output format to include risk predictions with severity levels, recommended actions, and a timeline for team intervention based on trend analysis.
                                  • C. Specify that the role is a test analyst with expertise in predictive analytics and risk management.
                                  • D. Add an instruction to calculate statistical variance and highlight tests that deviate by more than 20% from baseline metrics.
                                  Answer: B

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                                  Question #3

                                  What does an embedding represent in an LLM?

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

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                                  Question #4

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

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

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                                  Question #5

                                  Which statement BEST describes vision-language models (VLMs)?

                                  • A. VLMs are a subset of multimodal LLMs integrating visual and textual information.
                                  • B. VLMs process audio and video but not images.
                                  • C. VLMs are unrelated to multimodal LLMs and focus only on UI automation.
                                  • D. VLMs are a superset of multimodal LLMs.
                                  Answer: A

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