CT-GenAI Hot Questions - CT-GenAI Latest Test Dumps

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ISQI CT-GenAI Exam Syllabus Topics:

SectionObjectives
Topic 1: Application of GenAI in Software Testing- Practical use in testing workflows
  • 1. Regression suite optimization
    • 2. Defect report analysis and summarization
      • 3. Test case generation using LLMs
        • 4. Test data generation and augmentation
          Topic 2: 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 3: 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 4: Organizational Adoption and Governance- Enterprise GenAI adoption
                      • 1. Integration into CI/CD pipelines
                        • 2. Policy, ethics, and compliance considerations
                          • 3. LLMOps and governance models
                            Topic 5: 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

                                  >> CT-GenAI Hot Questions <<

                                  ISQI CT-GenAI Latest Test Dumps - Exam CT-GenAI PDF

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                                  ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 Sample Questions (Q29-Q34):

                                  NEW QUESTION # 29
                                  Which technique MOST directly reduces hallucinations by grounding the model in project realities?

                                  Answer: A

                                  Explanation:
                                  Hallucinations-where an LLM generates factually incorrect or nonsensical information-occur primarily when the model lacks sufficient specific information and "fills in the gaps" using probabilistic patterns from its training data. The most effective mitigation strategy is "grounding," which involves providing the model with detailed, project-specific context. By including technical specifications, existing API schemas, business rules, and identified constraints within the prompt, the tester restricts the model's operational space to the
                                  "project realities." This ensures the model does not have to guess or improvise details about the System Under Test (SUT). In contrast, randomizing prompts (Option B) or relying on generic examples (Option C) increases the likelihood of inconsistent and inaccurate outputs. Furthermore, using "longer" or higher temperature settings (Option D) actually encourages creativity and randomness, which is the opposite of the precision required for testing and significantly increases the risk of hallucinations. Therefore, rich contextual grounding is the technical foundation for reliable AI-assisted test analysis.


                                  NEW QUESTION # 30
                                  What BEST protects sensitive test data at rest and in transit?

                                  Answer: D

                                  Explanation:
                                  Data security is a paramount concern when using GenAI in testing, as test environments often contain sensitive business logic or PII (Personally Identifiable Information). To protect this data "at rest" (stored in databases or vector stores) and "in transit" (being sent to the LLM), a combination of technical controls is required.Role-Based Access Control (RBAC)is a fundamental security pillar that ensures only authorized individuals or services can access specific datasets or trigger GenAI workflows. This prevents unauthorized users from feeding sensitive enterprise data into public AI models. While encryption (omitted in Option A as an alternative to obfuscation) and TLS (falsely suggested to be disabled in Option C) are essential technical layers for protecting data in transit, RBAC provides the organizational "gatekeeping" necessary to manage who can interact with the AI system. In a professional GenAI strategy, testers must ensure that the tools they use adhere to strict access policies, ensuring that the "Input Data" used for prompting remains within the secured organizational boundary and is not leaked to unauthorized entities or public training sets.


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

                                  Answer: B


                                  NEW QUESTION # 32
                                  What does an embedding represent in an LLM?

                                  Answer: C

                                  Explanation:
                                  Embeddingsare a fundamental concept in modern Natural Language Processing (NLP) and LLMs. They are high-dimensional numerical vectors-essentially lists of numbers-that represent the meaning (semantics) of a piece of text (a word, sentence, or document). Unlike traditional keyword matching, which looks for identical strings of characters, embeddings allow the model to understand the "closeness" of concepts. For example, in a vector space, the word "bug" would be mathematically closer to "defect" or "error" than to
                                  "feature" or "requirement." This captures the semantic relationship between terms. This technology is the backbone of Retrieval-Augmented Generation (RAG) used in testing: when a tester queries a documentation set, the system converts the query into an embedding and looks for other chunks of text with similar vector values. This allows the AI to retrieve relevant context even if the exact keywords do not match. It is not about logical rules (Option C) or groups of tokens (Option A), but rather a mathematical representation of language that enables machines to process human meaning.


                                  NEW QUESTION # 33
                                  Which statement BEST describes vision-language models (VLMs)?

                                  Answer: C

                                  Explanation:
                                  Vision-Language Models (VLMs)represent a specialized subset of multimodal Large Language Models.
                                  Their defining characteristic is the ability to process, understand, and reason across both textual and visual modalities simultaneously. In the field of software testing, VLMs are revolutionary because they allow the AI to "see" a User Interface (UI). A tester can provide a screenshot of a web page alongside a natural language prompt, and the VLM can identify UI elements, detect visual regressions, or even validate that the visual layout matches a design specification. They are not a "superset" (Option C) of multimodal AI, but rather a specific implementation of it focused on the intersection of sight and language. Unlike traditional OCR or pixel-comparison tools used in legacy UI automation (Option B), VLMs understand thecontextof what they see-for instance, identifying a "broken" button icon that a human would recognize but a rule-based script might miss. This integration of visual and textual data is what makes them a vital component of modern, AI- augmented Quality Assurance strategies.


                                  NEW QUESTION # 34
                                  ......

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