CT-GenAI Current Exam Content & CT-GenAI Exam Overview

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ISQI CT-GenAI Exam Syllabus Topics:

SectionObjectives
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
          Organizational Adoption and Governance- Enterprise GenAI adoption
          • 1. Policy, ethics, and compliance considerations
            • 2. Integration into CI/CD pipelines
              • 3. LLMOps and governance models
                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
                    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
                          Risk, Quality, and Limitations of GenAI- Risks in GenAI usage
                          • 1. Data privacy and security concerns
                            • 2. Hallucinations and reasoning errors
                              • 3. Bias and fairness issues
                                • 4. Environmental and energy considerations

                                  >> CT-GenAI Current Exam Content <<

                                  CT-GenAI Exam Overview - CT-GenAI Latest Test Answers

                                  In today's highly competitive ISQI market, having the CT-GenAI certification is essential to propel your career forward. To earn the ISQI CT-GenAI certification, you must successfully pass the CT-GenAI Exam. However, preparing for the ISQI CT-GenAI exam can be challenging, with potential hurdles like exam anxiety and time constraints.

                                  ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 Sample Questions (Q37-Q42):

                                  NEW QUESTION # 37
                                  A tester uploads crafted images that steer the LLM into validating non-existent acceptance criteria. Which attack vector is this?

                                  Answer: A

                                  Explanation:
                                  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.


                                  NEW QUESTION # 38
                                  What distinguishes an LLM-powered agent from a basic AI chatbot in test processes?

                                  Answer: C

                                  Explanation:
                                  While a basic chatbot is primarily designed for textual interaction and information retrieval, anLLM- powered agent(or AI Agent) is characterized by itsagency-the ability to use tools and trigger actions in the external world. In a software testing context, an agent does not just "talk" about testing; it can actually perform testing tasks. For example, an agent could be given the goal to "verify the login module," and it would independently decide to call an API, generate a test script, execute it against a test environment, and then analyze the results to report a bug in Jira. This ability totrigger automated actions(Option C) through
                                  "function calling" or tool integration is what makes agents far more powerful than simple conversational interfaces (Option D). Agents can reason about "how" to achieve a goal, selecting the appropriate tools (like Selenium, Postman, or specialized internal utilities) to complete the task. This moves the AI from being a passive advisor to an active participant in the test automation ecosystem, requiring testers to focus more on goal definition and result validation.


                                  NEW QUESTION # 39
                                  What does an embedding represent in an LLM?

                                  Answer: A

                                  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 # 40
                                  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: D


                                  NEW QUESTION # 41
                                  Which standard specifies requirements for managing AI systems within an organization, supporting consistent GenAI use in testing?

                                  Answer: C

                                  Explanation:
                                  ISO/IEC 42001:2023is the international standard for an AI Management System (AIMS). It is designed to help organizations develop, provide, or use AI systems responsibly by providing a certifiable framework of requirements and controls. In a software testing context, this standard is vital for establishing governance, ensuring that GenAI tools are used consistently and ethically across the lifecycle.NIST AI RMF 1.0(Option B) is a highly respected framework, but it is a set of voluntary guidelines for managing risk, not a
                                  "requirement standard" for a management system.ISO/IEC 23053:2022(Option C) provides a general framework for AI using machine learning but lacks the comprehensive "management system" scope found in
                                  42001. Finally, theEU AI Act(Option D) is a regulation (law), not a technical standard. For a test organization looking to align its GenAI strategy with international best practices and achieve formal certification, ISO/IEC
                                  42001 is the definitive standard to follow, as it covers the organizational processes, data handling, and risk management necessary for high-quality AI operations.


                                  NEW QUESTION # 42
                                  ......

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