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Generative-AI-Leader시험대비최신덤프샘플문제다운로드
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Google인증Generative-AI-Leader시험의자격증은 여러분에 많은 도움이 되리라 믿습니다. 하시는 일에서 한층 더 업그레이드될 것이고 생활에서도 분명히 많은 도움이 될 것입니다. 자격증취득 즉 재산을 얻었죠.Google인증Generative-AI-Leader시험은 여러분이 it지식테스트시험입니다. KoreaDumps에서는 여러분의 편리를 위하여 KoreaDumps만의 최고의 최신의Google Generative-AI-Leader덤프를 추천합니다. KoreaDumps를 선택은 여러분이 최고의 선택입니다. KoreaDumps는 제일 전면적인Google Generative-AI-Leader인증시험자료의 문제와 답을 가지고 잇습니다.
Google Generative-AI-Leader 시험요강:
| 주제 |
소개 |
| 주제 1 |
- 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.
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| 주제 2 |
- 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.
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| 주제 3 |
- 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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| 주제 4 |
- 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.
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>> Generative-AI-Leader시험대비 <<
시험대비 Generative-AI-Leader시험대비 덤프 샘플문제 다운
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최신 Google Cloud Certified Generative-AI-Leader 무료샘플문제 (Q51-Q56):
질문 # 51
A company is exploring Google Agentspace to improve how its employees search for information on their enterprise systems and automate certain tasks. What is the key business advantage of using Agentspace?
- A. Enhanced real-time communication and collaboration among team members.
- B. More granular control over support team access and permissions for sensitive data.
- C. Greater interoperability with legacy software systems and databases.
- D. Improved productivity and data interaction using AI assistants and advanced document analysis.
정답：D
설명：
Google Agentspace (or similar agent platforms) is designed to empower employees with AI-powered assistants that can navigate and interact with enterprise systems, analyze documents, and automate tasks. This directly leads to improved employee productivity and more efficient data interaction by leveraging AI to streamline workflows and provide faster access to information.
질문 # 52
A pharmaceutical company's research and development department spends significant time manually reviewing new scientific papers to identify potential drug targets. They need a solution that can answer questions about these documents and provide summarized insights to researchers without requiring extensive coding expertise. What should the organization do?
- A. Use Vertex AI Agent Builder to create a custom AI agent.
- B. Use Gemini for Google Workspace to facilitate collaborative document review.
- C. Use Vertex AI AutoML to train a model that classifies papers into predefined research areas.
- D. Use Vertex AI Search to index the papers and enable keyword-based searches.
정답：A
설명：
The requirement is to answer questions about the documents and provide summarized insights without requiring extensive coding expertise. Vertex AI Agent Builder is designed precisely for creating custom AI agents, often with low-code or no-code capabilities, that can interact with and process large volumes of information like scientific papers. While Vertex AI Search could index papers for keyword searches, it doesn't directly answer questions or provide summarized insights in the same way a generative AI agent built with Agent Builder could. Gemini for Google Workspace is for collaborative work, not specifically for building custom AI agents for document analysis. Vertex AI AutoML is for training classification models, which is different from answering questions and summarizing.
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질문 # 53
A company has a machine learning project that involves diverse data types like streaming data and structured databases. How does Google Cloud support data gathering for this project?
- A. The Gemini app is the primary Google Cloud tool for directly collecting data.
- B. Google Cloud's strengths are in the data analysis tools such as BigQuery.
- C. Google Cloud relies on Vertex AI to connect to external data.
- D. Google Cloud provides tools such as Pub/Sub, Cloud Storage, and Cloud SQL.
정답：D
설명：
Google Cloud offers a comprehensive suite of services for data ingestion and storage. Pub/Sub is for streaming data, Cloud Storage for various file types (including unstructured), and Cloud SQL for relational structured databases. These are fundamental for gathering diverse data. Gemini is a model, BigQuery is for analysis, and Vertex AI is for ML platform, not primary data collection tools themselves.
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질문 # 54
A social media platform uses a generative AI model to automatically generate summaries of user-submitted posts to provide quick overviews for other users. While the summaries are generally accurate for factual posts, the model occasionally misinterprets sarcasm, satire, or nuanced opinions, leading to summaries that misrepresent the original intent and potentially cause misunderstandings or offense among users. What should the platform do to overcome this limitation of the AI-generated summaries?
- A. Increase the temperature parameter of the model to encourage more varied and less literal interpretations.
- B. Implement stricter safety settings to filter out potentially misinterpreted content altogether.
- C. Incorporate a human-in-the-loop (HITL) review process to refine the summaries.
- D. Decrease the output length of the summaries to make them more concise.
정답：C
설명：
When AI struggles with nuances like sarcasm or satire, human oversight is often the most effective solution. A human-in-the-loop (HITL) process allows human reviewers to check, correct, and refine AI-generated content before it is published, ensuring accuracy and appropriateness, especially for sensitive or complex language.
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질문 # 55
A company is developing a conversational AI chatbot. They need to ensure the chatbot can engage in human- like conversations and provide accurate information. What should they do to enhance thechatbot's ability to understand and respond effectively to user prompts?
- A. Limit the chatbot's training data to prevent it from learning irrelevant information.
- B. Use strict keyword matching to ensure that the chatbot only responds to specific commands.
- C. Lower model temperature setting to produce more consistent and predictable responses.
- D. Use prompt engineering techniques, like few-shot prompting, to provide the chatbot with examples of successful interactions.
정답：D
설명：
Prompt engineering, especially techniques like few-shot prompting (providing examples of desired input- output pairs), is crucial for guiding a generative AI model to understand context and generate relevant, human- like responses. Limiting data or using strict keyword matching would severely restrict the chatbot's conversational ability, and lowering temperature makes responses less creative, not necessarily more understanding.
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질문 # 56
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