2026 Itexamdump 최신 CT-GenAI PDF 버전 시험 문제집과 CT-GenAI 시험 문제 및 답변 무료 공유: https://drive.google.com/open?id=1yBteYHHI2gXCyc_MUkFQdxne9PublY-T
Itexamdump의ISQI CT-GenAI 덤프 구매 후 등록된 사용자가 구매일로부터 일년 이내에ISQI CT-GenAI시험에 실패하셨다면 Itexamdump메일에 주문번호와 불합격성적표를 보내오셔서 환불신청하실수 있습니다.구매일자 이전에 발생한 시험불합격은 환불보상의 대상이 아닙니다. 개별 인증사는 불합격성적표를 발급하지 않기에 재시험신청내역을 환불증명으로 제출하시면 됩니다.
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: 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 |
| Topic 2: Prompt Engineering for Effective Software Testing | 35% | - Iterative refinement and evaluation of prompts - Principles and structure of effective prompts - Prompt patterns for test design, data generation, automation |
| Topic 3: Deploying and Integrating GenAI in Test Organisations | 15% | - Roles, skills, and team readiness - Strategy, governance, and adoption roadmap - Measuring value and continuous improvement |
| Topic 4: LLM-Powered Test Infrastructure | 10% | - Architecture and deployment considerations - AI agents and integration with test tools - RAG, fine-tuning, and model adaptation |
| Topic 5: Managing Risks of Generative AI in Software Testing | 25% | - Data privacy, security, and compliance concerns - Validation, verification, and mitigation strategies - Hallucinations, bias, inaccuracy, and consistency risks |
ISQI 인증CT-GenAI인증시험공부자료는Itexamdump에서 제공해드리는ISQI 인증CT-GenAI덤프가 가장 좋은 선택입니다. Itexamdump에서는 시험문제가 업데이트되면 덤프도 업데이트 진행하도록 최선을 다하여 업데이트서비스를 제공해드려 고객님께서소유하신 덤프가 시장에서 가장 최신버전덤프로 되도록 보장하여 시험을 맞이할수 있게 도와드립니다.
질문 # 38
Which statement about data privacy risks in GenAI-assisted testing is INCORRECT?
정답：C
설명：
The statement that "Strict GDPR compliance eliminates all privacy risk" isincorrectbecause compliance is a legal and procedural framework, not a foolproof technical shield against all possible risks. Even within a GDPR-compliant environment, risks such as "model inversion" attacks, accidental data leakage through
"membership inference," or the unintentional generation of Sensitive Personally Identifiable Information (SPII) can still occur. Data privacy in GenAI is complex because LLMs function by processing and sometimes retaining patterns from the data they are fed. As noted in the CT-GenAI syllabus, some tools may process data in ways that are not fully transparent (Option A), and outputs can inadvertently include snippets of sensitive data used during the prompting or training phase (Option B). Furthermore, failing to adhere to regulations like GDPR or the EU AI Act certainly leads to legal and financial exposure (Option D). Therefore, while compliance frameworks significantly mitigate risk, they do not "eliminate" it; a robust GenAI strategy requires ongoing technical controls, data masking, and human oversight to manage residual privacy threats effectively.
질문 # 39
Which statement BEST contrasts interaction style and scope?
정답：B
설명：
It is important to distinguish between a general-purposeChatbotand a specializedLLM applicationwithin a testing framework. A chatbot is primarily designed for multi-turn, conversational interactions where the user can ask questions and receive answers in a natural language format. While useful for general queries, it often lacks the specialized workflow integration needed for rigorous testing. Conversely,LLM applications(or
"LLM-powered tools") are built with a specific "scope" in mind, such as automated test generation, code analysis, or requirement mapping. These applications often use the LLM as an underlying engine but surround it with specific UI components, data connectors (like RAG), and fixed task-oriented prompts to achieve a defined testing outcome. While chatbots are "free-form," LLM apps are "capability-driven." This distinction is key for organizations defining a GenAI strategy; simply providing a chatbot to testers is rarely sufficient.
Instead, organizations should develop or adopt LLM applications that integrate directly into the CI/CD pipeline and provide structured, actionable test artifacts that support defined quality engineering tasks.
질문 # 40
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 # 41
A prompt begins: "You are a senior test manager responsible for risk-based test planning on a payments platform." Which component is this?
정답：C
설명：
In structured prompt engineering, theRolecomponent (also known as a Persona) is used to set the perspective, expertise, and tone of the LLM's response. By assigning the role of a "senior test manager," the tester instructs the model to adopt the specific domain knowledge, vocabulary, and professional standards associated with that position. This technique is highly effective because LLMs are trained on vast datasets containing diverse professional documents; invoking a specific persona helps the model narrow its "latent space" to retrieve information relevant to that specific field. For instance, a senior test manager persona will prioritize risk management, resource allocation, and high-level strategy, whereas a "junior developer" persona might focus more on syntax and local unit tests. WhileContext(Option B) provides the background of the project andInstruction(Option A) defines the specific task to be performed, theRoleserves as the foundation for how those instructions are interpreted. This ensures the generated testware aligns with the expected professional seniority and organizational maturity required for high-stakes environments like a payments platform.
질문 # 42
You must use GenAI to perform test analysis on a payments module with finalized requirements: (1) generate test conditions, (2) prioritize by risk, (3) check coverage gaps. Which sequence best applies prompt chaining?
정답：B
설명：
Prompt Chainingis a technique where a complex task is decomposed into several smaller, sequential steps, where the output of one step serves as the context or input for the next. This is far more reliable than a "one- shot" approach (Option A) because it reduces the cognitive load on the LLM and allows for intermediate verification. In the scenario of test analysis, the most logical and effective chain begins by extracting discrete test conditionsfrom the raw requirements. Once these conditions are established, the next "link" in the chain is toprioritize them based on risk(impact and likelihood), which requires the model to reason specifically about the importance of each condition. The final step is tomap these prioritized conditions back to the original requirementsto identify any "coverage gaps." This systematic flow (Option B) mirrors the professional test analysis process defined in the ISTQB/CT-GenAI standards. By following this sequence, the tester ensures that the AI-generated output is logically derived and thorough, providing a clear "audit trail" from the initial requirement to the final prioritized test suite.
질문 # 43
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