참고: Itcertkr에서 Google Drive로 공유하는 무료 2026 Google Generative-AI-Leader 시험 문제집이 있습니다: https://drive.google.com/open?id=1SU6HDbL0b-gyPhNrl_hIXOAdvZsSlEwg
네트워크 전성기에 있는 지금 인터넷에서Google 인증Generative-AI-Leader시험자료를 많이 검색할수 있습니다. 하지만 왜Itcertkr덤프자료만을 믿어야 할가요? Itcertkr덤프자료는 실제시험문제의 모든 유형에 근거하여 예상문제를 묶어둔 문제은행입니다.시험적중율이 거의 100%에 달하여Google 인증Generative-AI-Leader시험을 한방에 통과하도록 도와드립니다.
| Certification Vendor: | Google Cloud |
|---|---|
| Exam Name: | Generative AI Leader Certification Exam |
| Exam Number: | Generative-AI-Leader |
| Passing Score: | Not publicly disclosed |
| Exam Price: | USD 99 (plus tax where applicable) |
| Exam Duration: | 90 minutes |
| Available Languages: | Spanish, English, Japanese, Portuguese |
| Exam Format: | Multiple choice |
| Certificate Validity Period: | 3 years |
| Real Exam Qty: | 50-60 |
| Recommended Training: | Generative AI Leader Training Course Generative AI Leader Study Guide |
| Exam Registration: | Google Cloud Certification Registration |
| Sample Questions: | Google Generative-AI-Leader Sample Questions |
| Exam Way: | Online-proctored or onsite-proctored |
| Pre Condition: | No prerequisites required; open to all roles and backgrounds |
| Official Syllabus URL: | https://cloud.google.com/learn/certification/generative-ai-leader |
덤프는 구체적인 업데이트주기가 존재하지 않습니다. 하지만 저희는 수시로 Google Generative-AI-Leader 시험문제 변경을 체크하여Google Generative-AI-Leader덤프를 가장 최신버전으로 업데이트하도록 최선을 다하고 있습니다. Google Generative-AI-Leader덤프를 구매하면 1년간 업데이트될떼마다 최신버전을 구매시 사용한 메일로 전송해드립니다.
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질문 # 85
An order fulfillment team has an agent that automatically processes orders, updates inventory, sends shipping notifications, and handles returns. What type of agent is this?
정답：B
설명：
Generative AI agents are typically categorized based on the goal they are designed to achieve.
The agent described is performing a sequence of distinct, interconnected, operational tasks (processes orders, updates inventory, sends notifications, handles returns). These steps are typical components of a business workflow or process automation.
A Workflow Agent is an AI agent whose purpose is to automate and manage an entire business process or a complex multi-step sequence of operations that traditionally required manual handoffs between different systems or teams. It uses its large language model brain, coupled with tools (such as APIs to a CRM, Inventory database, or shipping system), to observe the state of a process (e.g., a new order), reason about the next step, and execute the necessary actions to move the process forward toward completion.
Customer Service Agents (C) and Conversational Agents (D) are focused on user interaction (chatbots, virtual assistants) rather than back-end transactional automation.
Employee Productivity Agents (B) typically focus on individual tasks like drafting emails, summarizing meetings, or internal search, not automating an end-to-end operational flow like order fulfillment.
Therefore, an agent designed to automate a complete, multi-step business process like order fulfillment is correctly classified as a Workflow Agent.
(Reference: Google Cloud Generative AI training materials categorize agents based on function, with Workflow Agents being those designed to automate multi-step business processes and operational sequences.)
질문 # 86
What is a definition of an AI agent?
정답：A
설명：
In the generative AI landscape, an AI Agent is distinct from a basic chatbot or standard foundation model because of its ability to act autonomously to execute multi-step objectives.
The correct definition is an application that learns how to achieve a goal based on inputs and tools available to it (C). An agent is built around a core Large Language Model (LLM) which serves as its " brain.
" Given an objective or goal from a user, the agent uses a reasoning loop (such as ReAct) to evaluate inputs, break the goal into sub-tasks, and call external tools (like APIs, web search, databases, or calculators) to interact with the external world and accomplish the task.
* Option A is incorrect because agents are dynamic and action-oriented, not static.
* Option B describes a " Human-in-the-loop " supervisor, not the AI agent itself.
* Option D describes a chat interface or frontend wrapper, which is merely a way to communicate with an agent, not the definition of the agent ' s functional architecture.
(Reference: Google Cloud ' s official architecture guides for Generative AI define an Agent as an autonomous software entity driven by an LLM that accepts natural language goals, breaks them down into executable workflows, and leverages tools to alter states or retrieve information to satisfy that goal.)
질문 # 87
A company wants to use generative AI to create a chatbot that can answer customer questions about their products and services. They need to ensure that the chatbot only uses information from the company's official documentation. What should the company do?
정답：A
설명：
Grounding is the technique of "grounding" the LLM's responses in specific, authoritative data sources (like the company's official documentation). This prevents the model from "hallucinating" or providing information outside of the approved knowledge base, ensuring accuracy and relevance to the company's specific products and services.
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질문 # 88
A language learning startup called VerbaQuest wants to improve outcomes for its learners.
Rather than a fixed syllabus, its app will use generative AI to observe each learner's quiz results in real time. When a learner has trouble with a grammar rule, the app immediately produces a simpler explanation and proposes a 5-question targeted drill. When the learner shows mastery, the app advances them to more challenging lessons and exercises. Which generative AI use case does this most closely reflect?
정답：C
설명：
This scenario describes an app that continuously tailors explanations and practice to each learner based on real time quiz performance. It simplifies instruction when a learner struggles and advances them when they demonstrate mastery. That is the essence of adaptivity and personalization because the system shapes the pace, difficulty, and content for each individual rather than following a fixed syllabus.
Generative AI is the mechanism that produces the customized explanations and targeted drills, yet the defining pattern is the closed loop of observing performance, deciding on the next best action for this learner, and delivering bespoke content. That full loop is what characterizes an adaptive and personalized learning experience.
질문 # 89
What are core hardware components of the infrastructure layer in the generative AI landscape?
정답：C
설명：
The Generative AI landscape is often broken down into several functional layers: Applications, Agents, Platforms, Models, and Infrastructure.
The Infrastructure Layer is the foundation, providing the physical and virtual computing resources necessary to run and train the large models. These resources include servers, storage, networking, and most importantly, the specialized hardware accelerators required for high-volume, parallel computation.
The core hardware components are the Graphics Processing Units (GPUs) and the custom-designed Tensor Processing Units (TPUs) (A). These accelerators are optimized for the massive matrix operations fundamental to deep learning and Gen AI model training and inference.
Options B (User interfaces) and D (Tools and services) refer to the Application and Platform layers, respectively.
Option C (Pre-trained models) refers to the Model layer.
The physical hardware underpinning these abstract layers are the TPUs and GPUs.
(Reference: Google Cloud Generative AI Study Guides state that the Infrastructure Layer provides the core computing resources needed for generative AI, including the physical hardware (like servers, GPUs, and TPUs) and the essential software needed to train, store, and run AI models.)
질문 # 90
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Generative-AI-Leader시험대비 공부문제: https://www.itcertkr.com/Generative-AI-Leader_exam.html
그 외, Itcertkr Generative-AI-Leader 시험 문제집 일부가 지금은 무료입니다: https://drive.google.com/open?id=1SU6HDbL0b-gyPhNrl_hIXOAdvZsSlEwg