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Generative-AI-Leader덤프최신문제 & Generative-AI-Leader인증시험덤프

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Itcertkr는 여러분이Google 인증Generative-AI-Leader인증시험 패스와 추후사업에 모두 도움이 되겠습니다. Itcertkr제품을 선택함으로 여러분은 시간도 절약하고 돈도 절약하는 일석이조의 득을 얻을수 있습니다. 또한 구매후 일년무료 업데이트 버전을 받을수 있는 기회를 얻을수 있습니다. Google 인증Generative-AI-Leader 인증시험패스는 아주 어렵습니다. 자기에 맞는 현명한 학습자료 선택은 성공의 지름길을 내딛는 첫발입니다. 퍼펙트한 자료만이 시험에서 성공할수 있습니다. Itcertkr시험문제와 답이야 말로 퍼펙트한 자료이죠. Itcertkr Google 인증Generative-AI-Leader인증시험자료는 100% 패스보장을 드립니다.

>> Generative-AI-Leader덤프최신문제 <<

Generative-AI-Leader인증 시험덤프 - Generative-AI-Leader최신 시험덤프자료

최근 IT 업종에 종사하는 분들이 점점 늘어가는 추세하에 경쟁이 점점 치열해지고 있습니다. IT인증시험은 국제에서 인정받는 효력있는 자격증을 취득하는 과정으로서 널리 알려져 있습니다. Itcertkr의 Google인증 Generative-AI-Leader덤프는IT인증시험의 한 과목인 Google인증 Generative-AI-Leader시험에 대비하여 만들어진 시험전 공부자료인데 높은 시험적중율과 친근한 가격으로 많은 사랑을 받고 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

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 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.

최신 Google Cloud Certified Generative-AI-Leader 무료샘플문제 (Q22-Q27):

질문 # 22
A finance team wants to use Gemma to help with daily tasks so that the financial analysts can focus on other work. Which business problem can Gemma most efficiently address?

A. The challenge of efficiently producing high-quality written summaries and initial drafts of financial communications.
B. The struggle to accurately extract key financial figures and insights from a variety of document formats, such as balance sheets and income statements, for quick reporting.
C. The difficulty in analyzing large datasets of financial transactions and market data to identify anomalies and predict future financial performance.
D. The complexity of building and deploying sophisticated internal knowledge bases to answer employees' finance-related questions with accurate and up-to-date information.

정답：A

설명：
Gemma is a family of lightweight, open-source Large Language Models (LLMs) from Google that are based on the same research and technology as the Gemini models. As an LLM, its core strength lies in language-based tasks, particularly the generation and summarization of text.
The problem that Gemma, or any pure LLM, can most efficiently address is:
Generating text: creating new content quickly (Option D).
Summarizing text: condensing long communications or documents (Option D).
Option D, producing high-quality written summaries and initial drafts, is a natural language generation task that aligns perfectly with the core function of an LLM like Gemma. It is a key productivity booster for analysts needing to draft reports or emails quickly.
Option B (Analyzing large datasets/predicting performance) requires traditional machine learning (ML) models or analytical tools like BigQuery ML, as LLMs are not specialized for numerical predictive modeling.
Option C (Extracting key financial figures from documents) is a task for a highly specialized tool like Google's Document AI.
Option A (Building internal knowledge bases for Q&A) is a broader use case that is best solved with a platform solution using RAG, such as Vertex AI Search, not just a base model.
(Reference: Google's description of the Gemma model family emphasizes its role as a flexible, open LLM that excels at language fundamentals, making it ideal for content creation, summarization, and other text generation tasks.)

질문 # 23
What does Model Garden enable a company to do?

A. Discover, customize, and deploy existing models from Google and its partners.
B. Manage different versions of a model, including the code, data, and parameters used to train it.
C. Evaluate the performance of different models using various metrics.
D. Train new models from scratch using large datasets.

정답：A

설명：
Model Garden is a key component of the Vertex AI Platform on Google Cloud, positioned as an AI/ML model library. Its core function is to provide a central, organized place for users to find and utilize a wide variety of machine learning assets.
Specifically, Model Garden enables customers to:
Discover a curated collection of models, including Google's latest Foundation Models (like Gemini and Imagen), specialized models, and enterprise-ready models from Google partners and the open-source community (e.g., Gemma).
Test and customize these models, often with tools like Vertex AI Studio for prompt tuning or fine-tuning with custom data.
Deploy the selected and customized models directly to applications with a consistent deployment pattern.
Options B and C describe features of other MLOps tools within Vertex AI (Model Evaluation and Model Registry/Metadata Management). Option D describes the Custom Training service within Vertex AI. Model Garden's unique value proposition is acting as the starting point: a marketplace or repository to discover and immediately deploy or customize existing, pre-trained models.
(Reference: Google Cloud documentation states that Model Garden on Vertex AI is a place to discover, test, customize, and deploy a wide variety of models from Google and Google partners, including first-party and open-source models.)

질문 # 24
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?

A. Adjust the temperature parameter.
B. Use grounding.
C. Use role prompting.
D. Use prompt chaining.

정답：B

설명：
The core requirement is to guarantee that the chatbot only uses information from the company's official documentation and does not rely on its general knowledge base. This is crucial for ensuring factual accuracy, relevance to the company's specific products, and preventing the generation of fabricated or incorrect information (hallucinations).
The specific technique designed to address this challenge is Grounding. Grounding is the process of connecting the Large Language Model's (LLM's) responses to a trusted, verifiable source of information, such as an organization's internal documents, databases, or live data feeds. When an LLM is grounded, it is forced to base its answers only on the provided context, effectively preventing it from drawing on its broad, generalized training data. Grounding is often implemented using a method called Retrieval-Augmented Generation (RAG), particularly with tools like Google Cloud's Vertex AI Search, which indexes the official documentation and feeds the relevant snippets to the model.
Options A, B, and C address different aspects of model output: Role prompting sets the model's persona, adjusting temperature controls creativity, and prompt chaining manages conversation history, but none of these techniques restrict the model's source of truth to the official documentation. Therefore, Grounding is the correct and most effective technique for this requirement.

질문 # 25
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. Top-down strategy
C. Bottom-up strategy
D. Multi-directional strategy

정답：B

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 # 26
A highly regulated financial institution wants to use Gemini as the core decision engine for a loan approval system that will deterministically approve or reject loan applications based on a strict set of predefined criteria. Why is this an inappropriate use case for Gemini?

A. Gemini is designed for flexible content generation and inference, not rigid rule-based decisions.
B. Gemini deployment for this scenario would be too expensive and complex.
C. Gemini is not equipped to handle structured numerical data for financial assessments.
D. Gemini cannot integrate with required financial databases.

정답：A

설명：
Gemini, as a large language model, excels at flexible content generation, summarization, understanding, and inference. However, it is not designed for deterministic, rule-based decision-making that requires absolute consistency and adherence to strict, predefined criteria, as is common in highly regulated financial systems like loan approvals. Such systems typically require traditional programming logic or specific rule engines for auditable and consistent outcomes.
________________________________________

질문 # 27
......

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Generative-AI-Leader인증 시험덤프: https://www.itcertkr.com/Generative-AI-Leader_exam.html

참고: Itcertkr에서 Google Drive로 공유하는 무료, 최신 Generative-AI-Leader 시험 문제집이 있습니다: https://drive.google.com/open?id=1V5mBpt6yaNEqXcHux3zT1RR_oW8wXBQQ

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