Hugh Ross Hugh Ross's Profile Page

Hugh Ross Hugh Ross

0 Course Enrolled • 0 Course Completed

Biography

Generative-AI-Leader최고품질덤프샘플문제다운 & Generative-AI-Leader자격증참고서

Google Generative-AI-Leader 덤프결제에 관하여 불안정하게 생각되신다면 paypal에 대해 알아보시면 믿음이 생길것입니다. 더욱 안전한 지불을 위해 저희 사이트의 모든 덤프는paypal을 통해 지불을 완성하게 되어있습니다. Paypal을 거쳐서 지불하면 저희측에서Google Generative-AI-Leader덤프를 보내드리지 않을시 paypal에 환불신청하실수 있습니다.

IT인증시험을 쉽게 취득하는 지름길은Fast2test에 있습니다. Fast2test의Google인증 Generative-AI-Leader덤프로 시험준비를 시작하면 성공에 가까워집니다. Google인증 Generative-AI-Leader덤프는 최신 시험문제 출제방향에 대비하여 제작된 예상문제와 기출문제의 모음자료입니다. Google인증 Generative-AI-Leader덤프는 시험을 통과한 IT업계종사자분들이 검증해주신 세련된 공부자료입니다. Fast2test의Google인증 Generative-AI-Leader덤프를 공부하여 자격증을 땁시다.

>> Generative-AI-Leader최고품질 덤프샘플문제 다운 <<

Generative-AI-Leader자격증참고서 - Generative-AI-Leader시험패스 인증공부자료

우리 Fast2test에서는 최고이자 최신의Google 인증Generative-AI-Leader덤프자료를 제공 함으로 여러분을 도와Google 인증Generative-AI-Leader인증자격증을 쉽게 취득할 수 있게 해드립니다.만약 아직도Google 인증Generative-AI-Leader시험패스를 위하여 고군분투하고 있다면 바로 우리 Fast2test를 선택함으로 여러분의 고민을 날려버릴수 있습니다.

Google Generative-AI-Leader 시험요강:

주제
소개

주제 1

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 2

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.

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

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.

최신 Google Cloud Certified Generative-AI-Leader 무료샘플문제 (Q61-Q66):

질문 # 61
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 Search to index the papers and enable keyword-based searches.
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 Agent Builder to create a custom AI agent.

정답：D

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.
________________________________________

질문 # 62
A national bank is overwhelmed by customer inquiries across multiple channels and needs an AI-powered solution to provide seamless, consistent support, empower customer support agents, and improve service quality. What Google Cloud product should the bank use?

A. Gemini for Google Workspace
B. Google Contact Center as a Service
C. Gemini for Google Cloud
D. Vertex AI Search

정답：B

설명：
The bank's requirement is for a solution that provides seamless, consistent support across multiple channels and helps to empower customer support agents and improve service quality. This describes the need for a comprehensive, end-to-end customer service infrastructure.
Google Contact Center as a Service (CCaaS) is the full, cloud-native contact center solution offered by Google Cloud (part of the Customer Engagement Suite). It is specifically designed to unify customer interactions across various channels (phone, chat, web messaging) and provides the necessary infrastructure for routing, managing agent workflows, and ensuring a consistent and secure customer experience at scale. This solution goes beyond simply automating a chatbot.
While Vertex AI Search (A) can be used as a component within the solution to ground answers in an internal knowledge base, and Gemini for Google Workspace (B) can boost individual agent productivity, neither provides the comprehensive multi-channel contact center infrastructure that the scenario demands. The scale and nature of the problem-unifying overwhelmed support across channels and empowering agents-requires an enterprise-grade platform, which is precisely the function of Google Contact Center as a Service.

질문 # 63
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. Decrease the output length of the summaries to make them more concise.
B. Increase the temperature parameter of the model to encourage more varied and less literal interpretations.
C. Incorporate a human-in-the-loop (HITL) review process to refine the summaries.
D. Implement stricter safety settings to filter out potentially misinterpreted content altogether.

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.
________________________________________

질문 # 64
A company is defining their generative AI strategy. They want to follow Google-recommended practices to increase their chances of success. Which strategy should they use?

A. Rapid implementation strategy
B. Multi-directional strategy
C. Bottom-up strategy
D. Top-down strategy

정답：D

설명：
Google Cloud often recommends a "top-down" approach for generative AI strategy. This means starting with clear business objectives and leadership alignment on how generative AI can solve critical business problems, rather than simply experimenting from the bottom up without a clear strategic direction.
________________________________________

질문 # 65
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. Google Cloud's strengths are in the data analysis tools such as BigQuery.
B. The Gemini app is the primary Google Cloud tool for directly collecting data.
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.
________________________________________

질문 # 66
......

Fast2test전문가들은Google Generative-AI-Leader인증시험만을 위한 특별학습가이드를 만들었습니다.Google Generative-AI-Leader인증시험을 응시하려면 30분이란 시간만 투자하여 특별학습가이드로 빨리 관련지식을 장악하고,또 다시 복습하고 안전하게Google Generative-AI-Leader인증시험을 패스할 수 잇습니다.자격증취득 많은 시간과 돈을 투자한 분들보다 더 가볍게 이루어졌습니다

Generative-AI-Leader자격증참고서: https://kr.fast2test.com/Generative-AI-Leader-premium-file.html