KoreaDumps CT-GenAI 최신 PDF 버전 시험 문제집을 무료로 Google Drive에서 다운로드하세요: https://drive.google.com/open?id=16HJx8bvig-dJG1W_sIeVdrKAAhM3j8_x
KoreaDumps에서 제공해드리는 ISQI인증 CT-GenAI덤프는 가장 출중한ISQI인증 CT-GenAI시험전 공부자료입니다. 덤프품질은 수많은 IT인사들로부터 검증받았습니다. ISQI인증 CT-GenAI덤프뿐만아니라 KoreaDumps에서는 모든 IT인증시험에 대비한 덤프를 제공해드립니다. IT인증자격증을 취득하려는 분들은KoreaDumps에 관심을 가져보세요. 구매의향이 있으시면 할인도 가능합니다. 고득점으로 패스하시면 지인분들께 추천도 해주실거죠?
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Deploying and Integrating GenAI in Test Organisations | 15% | - Measuring value and continuous improvement - Strategy, governance, and adoption roadmap - Roles, skills, and team readiness |
| Topic 2: Prompt Engineering for Effective Software Testing | 35% | - Principles and structure of effective prompts - Prompt patterns for test design, data generation, automation - Iterative refinement and evaluation of prompts |
| Topic 3: LLM-Powered Test Infrastructure | 10% | - AI agents and integration with test tools - Architecture and deployment considerations - RAG, fine-tuning, and model adaptation |
| Topic 4: Managing Risks of Generative AI in Software Testing | 25% | - Hallucinations, bias, inaccuracy, and consistency risks - Validation, verification, and mitigation strategies - Data privacy, security, and compliance concerns |
| Topic 5: Introduction to Generative AI for Software Testing | 15% | - Capabilities and limitations relevant to testing - Use cases across the testing lifecycle - Core concepts: Generative AI, LLMs, foundation models |
ISQI CT-GenAI 시험탈락시ISQI CT-GenAI덤프비용전액을 환불해드릴만큼 저희 덤프자료에 자신이 있습니다. KoreaDumps에서는ISQI CT-GenAI덤프를 항상 최신버전이도록 보장해드리고 싶지만ISQI CT-GenAI시험문제변경시점을 예측할수 없어 시험에서 불합격받을수도 간혹 있습니다. 하지만 시험에서 떨어지면 덤프비용을 전액 환불해드려 고객님의 이익을 보장해드립니다.
질문 # 40
You must generate test cases for a new payments rule. The system includes API specifications stored in a vector database and prior tests in a relational database. Which of the following sequences BEST represents the correct order for applying a Retrieval-Augmented Generation (RAG) workflow?
i. Retrieve semantically similar specification chunks from the vector database ii. Feed both retrieved datasets as context for the LLM to generate new test cases iii. Retrieve relevant historical cases from the relational database iv. Submit a focused query describing the new test requirement
정답：C
설명：
A Retrieval-Augmented Generation (RAG) workflow is designed to "ground" an LLM's output in specific, verifiable data. The logical flow begins with an initial input or "focused query" (Step iv) that defines the tester's goal-in this case, generating cases for a new payments rule. The system then uses this query to perform a semantic search in avector database(Step i) to find the most relevant "chunks" of the new API specification. Following this, the system retrieves complementary data from therelational database(Step iii), such as historical test cases that might provide structural patterns or regression context. Finally, all the retrieved information-the new specs and the historical context-is bundled together and "fed" into the LLM as part of an augmented prompt (Step ii). This ensures the LLM doesn't hallucinate rules but instead synthesizes the new requirements with established organizational testing standards. Following the order in Option B ensures that the model is provided with the most relevant and logically organized context prior to generating the final testware.
질문 # 41
What is a primary compliance concern related to Shadow AI in organizational test environments?
정답：A
설명：
Shadow AIrefers to the use of artificial intelligence tools and services within an organization without explicit approval or oversight from the IT or Security departments. In a software testing environment, this often occurs when testers use public, consumer-grade LLMs to analyze proprietary code or sensitive requirement documents to speed up their work. The primary compliance concern is theviolation of established data handling and regulatory compliance standards(such as GDPR, HIPAA, or SOC2). When sensitive test data is fed into a "shadow" AI tool, that data may be stored on external servers or used to train future iterations of the model, leading to massive data leaks and legal exposure. This bypasses the organization's security controls, such as data masking and role-based access. Unlike "authorized" AI which undergoes a rigorous vendor risk assessment, Shadow AI creates an invisible attack surface. For a test organization, mitigating this risk involves providing approved, secure AI alternatives and implementing strict policies and monitoring to ensure that internal intellectual property is never processed by unvetted external services.
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
What does an embedding represent in an LLM?
정답：D
설명：
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.
질문 # 44
You are using an LLM to assist in analyzing test execution trends to predict potential risks. Which of the following improvements would BEST enhance the LLM's ability to predict risks and provide actionable alerts?
정답：A
설명：
The effectiveness of an LLM is heavily dependent on the specificity of itsOutput Format. While role definition (Option C) and technical instructions (Option D) are helpful, the most significant "value add" for a test lead is receiving information that is directlyactionable. By expanding the output format to include structuredrisk predictions, severity levels, and recommended actions(Option B), the tester is forcing the LLM to perform a deeper level of analysis. Instead of just "flagging trends," the model must now synthesize the data to determinewhya trend is a risk andwhatthe team should do about it. This aligns with the "Advanced Prompting" section of the CT-GenAI syllabus, which emphasizes using AI for decision support. A structured report that includes a "timeline for intervention" allows the human tester to quickly validate the AI's logic and make informed decisions, transforming the LLM from a simple data summarizer into a strategic predictive tool that actively supports the maintenance of release quality and schedule adherence.
질문 # 45
......
ISQI인증 CT-GenAI시험은 멋진 IT전문가로 거듭나는 길에서 반드시 넘어야할 높은 산입니다. ISQI인증 CT-GenAI시험문제패스가 어렵다한들KoreaDumps덤프만 있으면 패스도 간단한 일로 변경됩니다. KoreaDumps의ISQI인증 CT-GenAI덤프는 100%시험패스율을 보장합니다. ISQI인증 CT-GenAI시험문제가 업데이트되면ISQI인증 CT-GenAI덤프도 바로 업데이트하여 무료 업데이트서비스를 제공해드리기에 덤프유효기간을 연장해는것으로 됩니다.
CT-GenAI완벽한 인증시험덤프: https://www.koreadumps.com/CT-GenAI_exam-braindumps.html
참고: KoreaDumps에서 Google Drive로 공유하는 무료 2026 ISQI CT-GenAI 시험 문제집이 있습니다: https://drive.google.com/open?id=16HJx8bvig-dJG1W_sIeVdrKAAhM3j8_x