CT-GenAI German - CT-GenAI Prüfungs-Guide

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ISQI CT-GenAI Exam Syllabus Topics:

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

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                                  ISQI CT-GenAI: ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 braindumps PDF & Testking echter Test

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                                  ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 CT-GenAI Prüfungsfragen mit Lösungen (Q25-Q30):

                                  25. Frage
                                  A tester uploads crafted images that steer the LLM into validating non-existent acceptance criteria. Which attack vector is this?

                                  Antwort: B

                                  Begründung:
                                  This scenario describes a form ofRequest Manipulation, specifically a type of "Prompt Injection" or
                                  "Adversarial Prompting." In this attack vector, the user (or an external attacker) provides malicious or deceptive input-in this case, via an image in a multimodal LLM-to bypass the model's intended constraints or to steer its logic toward an unintended outcome. By crafting an image that tricks the LLM into seeing
                                  "acceptance criteria" that aren't actually there, the attacker manipulates the model's request processing to generate false validation results. This is different fromData Poisoning(Option A), which involves corrupting the training data before the model is even built. It is also distinct fromData Exfiltration(Option B), which aims to steal data from the model. In a testing environment, request manipulation is a significant risk because it can lead to "Silent Failures," where the AI reports that tests have passed or requirements are met based on deceptive input, thereby compromising the integrity of the entire Quality Assurance process.


                                  26. Frage
                                  What does an embedding represent in an LLM?

                                  Antwort: D

                                  Begründung:
                                  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.


                                  27. Frage
                                  Which of the following is NOT a valid form of LLM-driven test data generation?

                                  Antwort: B

                                  Begründung:
                                  Generative AI is exceptionally capable of creating structured and unstructured data, but its role is limited to
                                  "generation" and "transformation," not infrastructure management or direct database administration. Creating production database backups (Option A) is a physical data management task involving the copying of actual stateful data from a server to storage; this is handled by database management systems (DBMS) and DevOps pipelines, not LLMs. Conversely, LLMs excel at the logic-based tasks listed in the other options. They can analyze requirements to identify and set boundary values (Option B) for input validation. They are also highly effective at creating combinatorial data (Option C), such as pairwise or all-combinations tables, by understanding the relationships between variables. Finally, one of the most powerful uses of GenAI in testing is generating synthetic datasets (Option D)-creating "fake" but realistically structured data that mimics production patterns without exposing Sensitive Personally Identifiable Information (SPII), thereby supporting privacy-compliant testing.


                                  28. Frage
                                  Your team needs to generate 500 API test cases for a REST API with 50 endpoints. You have documented 10 exemplar test cases that follow your organization's standard format. You want the LLM to generate test cases following the pattern demonstrated in your examples. Which of the following prompting techniques is BEST suited to achieve your goal in this scenario?

                                  Antwort: A

                                  Begründung:
                                  Few-shot promptingis the technique of providing a few examples (exemplars) within the prompt to demonstrate the desired task and output format to the LLM. In this scenario, providing 10 existing, high- quality test cases acts as a "pattern" for the model to follow. This is significantly more effective than "Zero- shot prompting" (Option D), where the model is given a task without examples and may deviate from the specific organizational format required (e.g., specific JSON structures or assertion styles). While "Prompt chaining" (Option A) is useful for breaking down complex tasks into sub-tasks, the primary need here is pattern recognition and replication, which is the core strength of Few-shot learning. "Meta prompting" (Option C) involves having the AI write the prompt itself, which is unnecessary when the team already has clear examples. By using Few-shot prompting, the tester "conditions" the model's latent space to prioritize the provided format, ensuring that all 500 generated test cases maintain consistency with the HTTP methods, headers, and assertion logic defined in the exemplars.


                                  29. Frage
                                  What distinguishes an LLM-powered agent from a basic AI chatbot in test processes?

                                  Antwort: C


                                  30. Frage
                                  ......

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                                  BONUS!!! Laden Sie die vollständige Version der ZertSoft CT-GenAI Prüfungsfragen kostenlos herunter: https://drive.google.com/open?id=1bVyt4o26GaIrcPcAP4lDEsHauDiLJ73E