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ISQI CT-GenAI Exam Syllabus Topics:

SectionObjectives
Topic 1: 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 2: Prompt Engineering for Testing- Prompt design techniques
      • 1. Prompt chaining and meta prompting
        • 2. Zero-shot, one-shot, few-shot prompting
          • 3. Structuring prompts for test case generation
            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: Organizational Adoption and Governance- Enterprise GenAI adoption
                    • 1. Integration into CI/CD pipelines
                      • 2. LLMOps and governance models
                        • 3. Policy, ethics, and compliance considerations
                          Topic 5: 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

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                                  ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 Sample Questions (Q28-Q33):

                                  NEW QUESTION # 28
                                  An attacker sends extremely long prompts to overflow context so the model leaks snippets from its training data. Which attack vector is this?

                                  Answer: A

                                  Explanation:
                                  This scenario describes a specialized form ofData Exfiltration(specifically targeting the model's internal
                                  "weights" or training memory). While data exfiltration usually refers to stealing data from a database, in the context of LLMs, it can also refer to techniques that force the model to "reveal" sensitive information it was trained on or data that exists within its current context window. By using long, repetitive, or specifically
                                  "crafted" prompts to overwhelm the model's normal attention mechanisms or safety filters, an attacker may cause the model to output verbatim snippets of proprietary information, PII, or internal documentation that should have remained confidential. This is different fromRequest Manipulation(Option D), which aims to change the model's behavior, orData Poisoning(Option A), which happens during training. In testing, this risk is high when models are fine-tuned on private company repositories. Testers must be aware that if a model is accessible to unauthorized users, those users might use adversarial prompting techniques to extract sensitive code or business logic through these types of data leakage attacks.


                                  NEW QUESTION # 29
                                  Which competency MOST helps testers steer LLMs to produce useful, on-policy testware?

                                  Answer: D

                                  Explanation:
                                  As Generative AI becomes integrated into the software testing lifecycle, the role of the tester shifts from manual authoring to the "orchestration" of AI models. Mastering prompt engineering is the primary competency required to effectively steer LLMs. Prompt engineering involves the deliberate design of inputs- incorporating roles, context, instructions, and constraints-to elicit the most accurate and "on-policy" outputs from the model. In a testing context, "on-policy" refers to testware that adheres to organizational standards, security protocols, and specific project requirements. While technical skills like network configuration or low- level programming (Options B, C, and D) are valuable in specific engineering domains, they do not directly influence the communicative interface between the human and the AI. A tester proficient in prompt engineering can utilize techniques like "Chain-of-Thought" or "Few-shot prompting" to ensure the LLM understands the nuances of a test plan, thereby reducing hallucinations and ensuring the generated test cases are actionable, relevant, and compliant with the project's quality gates.


                                  NEW QUESTION # 30
                                  A prompt section states: "Web checkout module v3.2; focus on coupon application; existing regression suite IDs T-112-T-150; recent defect ID BUG-431." Which component is this?

                                  Answer: D

                                  Explanation:
                                  In a structured prompt, "Input Data" (or Reference Data) provides the specific subject matter that the model must process or analyze. The statement provided consists of factual identifiers and specific entities related to the System Under Test (SUT), such as the version number, the specific module name, reference IDs for existing tests, and a specific defect record. These elements serve as the raw material for the LLM's task. This differs from "Instructions" (Option C), which would be the command (e.g., "Analyze the following..."), or
                                  "Constraints" (Option B), which would define the boundaries of the task (e.g., "Do not include T-115").
                                  "Output Format" (Option D) would define how the result should look (e.g., "Provide a JSON list"). By clearly labeling this section as Input Data, the tester helps the model distinguish between the "what" (the data) and the "how" (the instructions), which is a key principle of structured prompt engineering aimed at improving the accuracy of AI-generated analysis.


                                  NEW QUESTION # 31
                                  What distinguishes an LLM-powered agent from a basic AI chatbot in test processes?

                                  Answer: C


                                  NEW QUESTION # 32
                                  What is a primary compliance concern related to Shadow AI in organizational test environments?

                                  Answer: D

                                  Explanation:
                                  Shadow AIrefers to the use of artificial intelligence tools and services within an organization without explicit approval or oversight from the IT or Security departments. In a software testing environment, this often occurs when testers use public, consumer-grade LLMs to analyze proprietary code or sensitive requirement documents to speed up their work. The primary compliance concern is theviolation of established data handling and regulatory compliance standards(such as GDPR, HIPAA, or SOC2). When sensitive test data is fed into a "shadow" AI tool, that data may be stored on external servers or used to train future iterations of the model, leading to massive data leaks and legal exposure. This bypasses the organization's security controls, such as data masking and role-based access. Unlike "authorized" AI which undergoes a rigorous vendor risk assessment, Shadow AI creates an invisible attack surface. For a test organization, mitigating this risk involves providing approved, secure AI alternatives and implementing strict policies and monitoring to ensure that internal intellectual property is never processed by unvetted external services.


                                  NEW QUESTION # 33
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