인기자격증Generative-AI-Leader높은통과율시험덤프자료시험최신덤프자료

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Google Generative-AI-Leader 시험요강:

주제소개
주제 1
  • Fundamentals of Generative AI: This section of the exam measures the skills of AI Engineers and focuses on the foundational concepts of generative AI. It covers the basics of artificial intelligence, natural language processing, machine learning approaches, and the role of foundation models. Candidates are expected to understand the machine learning lifecycle, data quality, and the use of structured and unstructured data. The section also evaluates knowledge of business use cases such as text, image, code, and video generation, along with the ability to identify when and how to select the right model for specific organizational needs.
주제 2
  • Techniques to Improve Generative AI Model Output: This section of the exam measures the skills of AI Engineers and focuses on improving model reliability and performance. It introduces best practices to address common foundation model limitations such as bias, hallucinations, and data dependency, using methods like retrieval-augmented generation, prompt engineering, and human-in-the-loop systems. Candidates are also tested on different prompting techniques, grounding approaches, and the ability to configure model settings such as temperature and token count to optimize results.
주제 3
  • Google Cloud’s Generative AI Offerings: This section of the exam measures the skills of Cloud Architects and highlights Google Cloud’s strengths in generative AI. It emphasizes Google’s AI-first approach, enterprise-ready platform, and open ecosystem. Candidates will learn about Google’s AI infrastructure, including TPUs, GPUs, and data centers, and how the platform provides secure, scalable, and privacy-conscious solutions. The section also explores prebuilt AI tools such as Gemini, Workspace integrations, and Agentspace, while demonstrating how these offerings enhance customer experience and empower developers to build with Vertex AI, RAG capabilities, and agent tooling.
주제 4
  • Business Strategies for a Successful Generative AI Solution: This section of the exam measures the skills of Cloud Architects and evaluates the ability to design, implement, and manage enterprise-level generative AI solutions. It covers the decision-making process for selecting the right solution, integrating AI into an organization, and measuring business impact. A strong emphasis is placed on secure AI practices, highlighting Google’s Secure AI Framework and cloud security tools, as well as the importance of responsible AI, including fairness, transparency, privacy, and accountability.

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최신 Google Cloud Certified Generative-AI-Leader 무료샘플문제 (Q68-Q73):

질문 # 68
A company is developing an AI character for a video game. The AI character needs to learn how to navigate a complex environment and make decisions to achieve certain objectives within the game. When the AI takes actions that lead to positive outcomes, like finding a reward or overcoming an obstacle, it receives a positive score. When it takes actions that lead to negative outcomes, like hitting a wall or losing progress, it receives a negative score. Through this process of trial and error, the AI gradually improves the character's ability to play the game effectively.
What machine learning should the company use?

정답：B


질문 # 69
A market research firm wants to use a Google Cloud prebuilt generative AI offering to streamline the process of extracting and synthesizing information from lengthy market reports and research papers. Their goal is to improve efficiency and provide faster insights to their clients. What should the organization do?

정답：D

설명：
NotebookLM is a prebuilt, source-grounded research and synthesis tool designed for working with uploaded documents and other selected sources. The firm can provide its market reports and research papers, ask questions about their contents, generate summaries, compare information, and identify important themes.
Because responses are grounded in the supplied sources, researchers can obtain focused insights more efficiently while retaining the ability to review supporting material. The standard Gemini app can provide general assistance but is less specifically optimized for a defined document collection. Building custom conversational agents would introduce unnecessary development effort when a prebuilt offering satisfies the requirement. Gemini in Google Workspace can assist with drafting and collaboration, but the central need is extracting and synthesizing information from lengthy source documents. NotebookLM is therefore the most appropriate choice.


질문 # 70
The office of the CISO wants to use generative AI (gen AI) to help automate tasks like summarizing case information, researching threats, and taking actions like creating detection rules. What agent should they use?

정답：C

설명：
Given the tasks involve researching threats and creating detection rules, the most appropriate and specialized agent would be a Security agent. This type of agent would be pre-configured or easily adaptable to understand security-specific contexts, data, and actions within a CISO's domain.


질문 # 71
A company wants to use AI to automate a complex business process that involves multiple steps and requires access to different systems. They need something that can handle multi-step tasks, such as researching a topic, troubleshooting code, and accessing a system by chaining together actions. What part of the gen AI landscape can handle multi-step tasks?

정답：B

설명：
In the generative AI taxonomy:
Agents are autonomous or semi-autonomous systems that reason, plan, break goals down into multi-step sequences, call external APIs/tools, and chain actions together to complete end-to-end tasks.
Models (e.g., LLMs) serve as the underlying intelligence or reasoning engine.
Platforms provide the development environment, orchestration frameworks, and governance tools.
Infrastructure provides hardware accelerators (TPUs/GPUs), storage, and networking.


질문 # 72
An organization is building a generative AI agent for employee travel bookings. The agent needs to connect to external flight and hotel systems for availability, pricing, and reservations. They implement the most simple and effective way for the agent to interact with the external travel provider system, following Google Cloud-recommended practices. What method should they use?

정답：B

설명：
Extensions provide a standardized mechanism through which an AI agent can invoke external APIs and complete real-world actions. A travel extension can connect the agent to flight and hotel services, pass structured parameters, retrieve current availability and prices, and submit reservations. This is more maintainable and consistent than embedding separate custom functions directly inside the agent. Pre-loaded data stores cannot satisfy the requirement because availability and pricing change continuously, while reservations require transactional access to the provider's live system. "Plugins" is not the recommended Google Cloud mechanism identified for this agent-integration scenario. Extensions enable agents to move beyond answering questions by securely communicating with external services through defined interfaces. Consequently, they are the simplest and most effective Google-recommended method for integrating the travel agent with provider APIs.


질문 # 73
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