참고: Itexamdump에서 Google Drive로 공유하는 무료 2026 ISQI CT-GenAI 시험 문제집이 있습니다: https://drive.google.com/open?id=1yBteYHHI2gXCyc_MUkFQdxne9PublY-T
발달한 네트웨크 시대에 인터넷에 검색하면 많은ISQI인증 CT-GenAI시험공부자료가 검색되어 어느 자료로 시험준비를 해야 할지 망서이게 됩니다. 이 글을 보는 순간 다른 공부자료는 잊고Itexamdump의ISQI인증 CT-GenAI시험준비 덤프를 주목하세요. 최강 IT전문가팀이 가장 최근의ISQI인증 CT-GenAI 실제시험 문제를 연구하여 만든ISQI인증 CT-GenAI덤프는 기출문제와 예상문제의 모음 공부자료입니다. Itexamdump의ISQI인증 CT-GenAI덤프만 공부하면 시험패스의 높은 산을 넘을수 있습니다.
| Section | Weight | Objectives |
|---|---|---|
| Deploying and Integrating GenAI in Test Organisations | 15% | - Roles, skills, and team readiness - Measuring value and continuous improvement - Strategy, governance, and adoption roadmap |
| Prompt Engineering for Effective Software Testing | 35% | - Prompt patterns for test design, data generation, automation - Iterative refinement and evaluation of prompts - Principles and structure of effective prompts |
| Managing Risks of Generative AI in Software Testing | 25% | - Validation, verification, and mitigation strategies - Hallucinations, bias, inaccuracy, and consistency risks - Data privacy, security, and compliance concerns |
| Introduction to Generative AI for Software Testing | 15% | - Use cases across the testing lifecycle - Core concepts: Generative AI, LLMs, foundation models - Capabilities and limitations relevant to testing |
| LLM-Powered Test Infrastructure | 10% | - Architecture and deployment considerations - RAG, fine-tuning, and model adaptation - AI agents and integration with test tools |
Itexamdump 에서 출시한ISQI인증CT-GenAI 덤프는ISQI인증CT-GenAI 실제시험의 출제범위와 출제유형을 대비하여 제작된 최신버전 덤프입니다. 시험문제가 바뀌면 제일 빠른 시일내에 덤프를 업데이트 하도록 최선을 다하고 있으며 1년 무료 업데이트서비스를 제공해드립니다. 1년 무료 업데이트서비스를 제공해드리기에 시험시간을 늦추어도 시험성적에 아무런 페를 끼치지 않습니다. Itexamdump에 믿음을 느낄수 있도록 구매사이트마다 무료샘플 다운가능기능을 설치하였습니다.무료샘플을 체험해보시고Itexamdump을 선택해주세요.
질문 # 36
What distinguishes an LLM-powered agent from a basic AI chatbot in test processes?
정답：A
설명：
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.
질문 # 37
A tester uploads crafted images that steer the LLM into validating non-existent acceptance criteria. Which attack vector is this?
정답：A
설명：
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.
질문 # 38
Which standard specifies requirements for managing AI systems within an organization, supporting consistent GenAI use in testing?
정답：A
설명：
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.
질문 # 39
What is a hallucination in LLM outputs?
정답：C
설명：
A hallucination refers to a phenomenon where a Large Language Model generates text that is grammatically correct and seemingly plausible but is factually incorrect or unsupported by the provided context or real-world data. In the context of software testing, this is a critical limitation. For example, an LLM might generate a test case for a software feature that does not exist or cite a non-existent API parameter. These errors occur because LLMs are probabilistic engines designed to predict the "most likely" next token rather than "reasoning" from a set of verified facts. They do not have a built-in "truth" mechanism. While a logical mistake (Option B) is a failure in reasoning and a systematic preference (Option D) describes bias, a hallucination is specifically about the fabrication of information. Testers must be particularly vigilant regarding hallucinations, as they can lead to "false confidence" in test coverage or the creation of invalid bug reports. Mitigations include grounding the model with Retrieval-Augmented Generation (RAG) and implementing rigorous "human-in-the- loop" verification of all AI-generated test artifacts.
질문 # 40
In the context of software testing, which statements (i-v) about foundation, instruction-tuned, and reasoning LLMs are CORRECT?
i. Foundation LLMs are best suited for broad exploratory ideation when test requirements are underspecified.
ii. Instruction-tuned LLMs are strongest at adhering to fixed test case formats (e.g., Gherkin) from clear prompts.
iii. Reasoning LLMs are strongest at multi-step root-cause analysis across logs, defects, and requirements.
iv. Foundation LLMs are optimal for strict policy compliance and template conformance.
v. Instruction-tuned LLMs can follow stepwise reasoning without any additional training or prompting.
정답：B
설명：
Understanding the hierarchy of LLM types is vital for selecting the right tool for specific testing tasks.
Foundation LLMsare trained on massive datasets to predict the next token; they excel at broad, creative
"ideation" (Statement i) but often struggle with following specific instructions or constraints (making Statement iv incorrect).Instruction-tuned LLMshave undergone additional training (Fine-tuning) to follow explicit commands and templates. They are highly effective at structured tasks like converting requirements into Gherkin feature files (Statement ii).Reasoning LLMs(or those utilizing specialized prompting like Chain- of-Thought) are designed to handle complex, multi-stage logic. This makes them the superior choice for diagnostic tasks like root-cause analysis, where the model must synthesize information across logs and requirements to find a defect's origin (Statement iii). Statement v is incorrect because while instruction-tuned models are capable, complex "stepwise reasoning" usually requires specific prompting techniques or the inherent logic of specialized reasoning models. Therefore, the combination of i, ii, and iii represents the correct alignment of model capability to testing functionality.
질문 # 41
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많은 분들이ISQI CT-GenAI시험을 패스하려고 하는데 시험대비방법을 찾지 못하고 계십니다. ISQI CT-GenAI덤프를 구매하려면 먼저ISQI CT-GenAI샘플문제를 다운받아 덤프품질을 검증후 주문하시면 믿음이 생길것입니다. ISQI CT-GenAI시험대비덤프는 IT업계에 오랜 시간동안 종사한 전문가들의 노하우로 연구해낸 최고의 자료입니다.
CT-GenAI시험패스 가능 덤프문제: https://www.itexamdump.com/CT-GenAI.html
그리고 Itexamdump CT-GenAI 시험 문제집의 전체 버전을 클라우드 저장소에서 다운로드할 수 있습니다: https://drive.google.com/open?id=1yBteYHHI2gXCyc_MUkFQdxne9PublY-T