꿈을 안고 사는 인생이 멋진 인생입니다. 고객님의 최근의 꿈은 승진이나 연봉인상이 아닐가 싶습니다. ISQI인증 CT-GenAI시험은 IT인증시험중 가장 인기있는 국제승인 자격증을 취득하는데서의 필수시험과목입니다.그만큼 시험문제가 어려워 시험도전할 용기가 없다구요? 이제 이런 걱정은 버리셔도 됩니다. PassTIP의 ISQI인증 CT-GenAI덤프는ISQI인증 CT-GenAI시험에 대비한 공부자료로서 시험적중율 100%입니다.
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Risks and Testing Challenges for Generative AI | 30% | - Quality Risks Specific to Generative AI
|
| Topic 2: Testing Activities for Generative AI | 30% | - Model and Output Evaluation
|
| Topic 3: Fundamentals of Generative AI | 20% | - AI Terminology
|
| Topic 4: Tools for Testing Generative AI | 20% | - Using Tools for Common Testing Activities
|
우리는 고객이 첫 번째 시도에서ISQI CT-GenAI 자격증시험을 합격할수있다는 것을 약속드립니다. ISQI CT-GenAI 시험을 합격하여 자격증을 손에 넣는다면 취직 혹은 연봉인상 혹은 승진이나 이직에 확실한 가산점이 될것입니다. ISQI CT-GenAI시험 어려운 시험이지만 저희ISQI CT-GenAI덤프로 조금이나마 쉽게 따봅시다.
질문 # 36
Which factor MOST influences the overall energy consumption of a Generative AI model used in software testing tasks?
정답：C
설명：
The environmental impact and sustainability of AI are increasingly important considerations in software engineering. The overall energy consumption of an LLM during inference (when the model is actually being used by a tester) is most directly influenced by thenumber of tokens processed. Every token generated or analyzed requires a massive amount of floating-point operations within the GPU clusters of a data center.
Therefore, the "length" of the input prompt and the "length" of the AI's response are the primary drivers of the power draw and, consequently, the carbon intensity of the query. This is a crucial concept for "Green AI" initiatives in testing; more efficient prompting-such as avoiding unnecessarily verbose context or limiting output lengths-can lead to more sustainable testing practices. While data center location (Option B) affects thetypeof energy used (renewable vs. fossil fuel), it does not determine the model's accuracy. Similarly, while cloud platforms (Option D) and session durations (Option C) play roles in operational logistics, the mathematical workload tied to token count remains the fundamental unit of energy expenditure in Generative AI.
질문 # 37
What distinguishes an LLM-powered agent from a basic AI chatbot in test processes?
정답：C
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 # 38
Which competency MOST helps testers steer LLMs to produce useful, on-policy testware?
정답：B
설명：
As Generative AI becomes integrated into the software testing lifecycle, the role of the tester shifts from manual authoring to the "orchestration" of AI models. Mastering prompt engineering is the primary competency required to effectively steer LLMs. Prompt engineering involves the deliberate design of inputs- incorporating roles, context, instructions, and constraints-to elicit the most accurate and "on-policy" outputs from the model. In a testing context, "on-policy" refers to testware that adheres to organizational standards, security protocols, and specific project requirements. While technical skills like network configuration or low- level programming (Options B, C, and D) are valuable in specific engineering domains, they do not directly influence the communicative interface between the human and the AI. A tester proficient in prompt engineering can utilize techniques like "Chain-of-Thought" or "Few-shot prompting" to ensure the LLM understands the nuances of a test plan, thereby reducing hallucinations and ensuring the generated test cases are actionable, relevant, and compliant with the project's quality gates.
질문 # 39
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：D
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 # 40
Which statement about data privacy risks in GenAI-assisted testing is INCORRECT?
정답：A
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 # 41
......
경쟁이 치열한 IT업계에서 굳굳한 자신만의 자리를 찾으려면 국제적으로 인정받는 IT자격증 취득은 너무나도 필요합니다. ISQI인증 CT-GenAI시험은 IT인사들중에서 뜨거운 인기를 누리고 있습니다. PassTIP는 IT인증시험에 대비한 시험전 공부자료를 제공해드리는 전문적인 사이트입니다.한방에 쉽게ISQI인증 CT-GenAI시험에서 고득점으로 패스하고 싶다면PassTIP의ISQI인증 CT-GenAI덤프를 선택하세요.저렴한 가격에 비해 너무나도 높은 시험적중율과 시험패스율, 언제나 여러분을 위해 최선을 다하는PassTIP가 되겠습니다.
CT-GenAI퍼펙트 최신 덤프공부: https://www.passtip.net/CT-GenAI-pass-exam.html