Open the auxiliary access

Firefly Open Source Community

   Login   |   Register   |
New_Topic
Firefly Open Source Community › Embedded Board › AIO-3568J› Generative-AI-Leader認定内容 & Generative-AI-Leade ...
New Topic
Print Previous Topic Next Topic

[General] Generative-AI-Leader認定内容 & Generative-AI-Leader更新版

donfox317

137

Credits

0

Prestige

0

Contribution

registered members

Rank: 2

Credits
137

【General】 Generative-AI-Leader認定内容 & Generative-AI-Leader更新版

Posted at 2 hour before      View：3 | Replies：0        Print      Only Author   [Copy Link] 1#
P.S. Pass4TestがGoogle Driveで共有している無料かつ新しいGenerative-AI-Leaderダンプ：https://drive.google.com/open?id=14fNLDyfwSHebDuock8J0ODJLT6oesRfj
21世紀は情報の世紀です。 そのため、GoogleのGenerative-AI-Leader試験問題のフィールドには多くの変更があります。 彼らはまた、人々の生活と人間社会の運営方法を大きく変えています。 Generative-AI-Leader試験の準備をしている場合、弊社Pass4TestはこのWebサイトで最高の電子Generative-AI-Leader試験トレントを提供できます。 私たちのGenerative-AI-LeaderのGoogle Cloud Certified - Generative AI Leader Examテストトレントの指導の下で、あなたはトラブルを回避し、すべてをあなたの歩みに乗せることができると強く信じています。
Google Generative-AI-Leader 認定試験の出題範囲：
トピック出題範囲
トピック 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.
トピック 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.
トピック 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.
トピック 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.

Generative-AI-Leader更新版 & Generative-AI-Leader資格認定あなたはGenerative-AI-Leader試験を準備していて精確の資料がありませんなら、我々Pass4Testの資料を参考しましょう。我々はあなたが一発で試験に合格するのを保証します。我々は試験に対応する弊社のGenerative-AI-Leader問題集を継続してアップグレードしています。あなたの持っているすべての商品は一年の無料更新を得られています。あなたは十分の時間でGenerative-AI-Leader試験を準備することができます。
Google Cloud Certified - Generative AI Leader Exam 認定 Generative-AI-Leader 試験問題 (Q70-Q75):質問 # 70
What are core hardware components of the infrastructure layer in the generative AI landscape?
  • A. Pre-trained models
  • B. TPUs and GPUs
  • C. Tools and services for building AI models
  • D. User interfaces
正解：B
解説：
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.)

質問 # 71
A global news agency is developing a generative AI tool to quickly summarize breaking newsarticles as they emerge online. The goal is to provide their audience with rapid updates on fast-developing stories from various global sources. What Google Cloud solution should they use?
  • A. Grounding with Google Search
  • B. Vertex AI Natural Language API
  • C. BigQuery
  • D. Document AI
正解：A
解説：
For summarizing breaking news articles as they emerge online from various global sources, the generative AI model needs access to current, broad, and rapidly updating information. Grounding with Google Search allows the LLM to pull in the latest information from the web, ensuring the summaries are current and comprehensive. While Vertex AI Natural Language API can summarize text, it wouldn't inherently have access to the latest breaking news unless explicitly fed.
________________________________________

質問 # 72
A marketing team wants to use a foundation model to create social media and advertising campaigns. They want to create written articles and images from text. They lack deep AI expertise and need a versatile solution. Which Google foundation model should they use?
  • A. Gemma
  • B. Veo
  • C. Imagen
  • D. Gemini
正解：D
解説：
Gemini is Google's most advanced and multimodal foundation model, capable of understanding and generating various forms of content, including text and images, from a single prompt. Its versatility makes it suitable for marketing teams that need to create diverse campaign materials without deep AI expertise. Imagen is specifically for image generation, Gemma is a family of smaller, open models, and Veo is for video generation.
________________________________________

質問 # 73
According to Google-recommended practices, when should generative AI be used to automate tasks?
  • A. When tasks are complex and require strategic decision-making.
  • B. When tasks involve sensitive information or require human oversight
  • C. When tasks are highly creative and require original thought.
  • D. When tasks are repetitive and rule-based.
正解：D
解説：
The strategic value of Generative AI (Gen AI) in a business context, as taught in Google's courses, is primarily to enhance efficiency and productivity by taking over tasks that consume significant employee time.
Gen AI excels in automating tasks that:
Are repetitive and time-consuming, such as drafting initial emails, summarizing long documents, or generating code snippets. Automating these routine tasks (C) frees employees to focus on higher-value activities (like building customer relationships or strategic planning).
Involve the generation of new content based on patterns learned from large datasets (e.g., text, images, code).
Options A and D represent high-value, strategic work-highly creative or complex strategic decision-making-where human judgment and oversight remain paramount. While Gen AI can assist with these (e.g., brainstorming creative ideas or providing data-backed insights), it is generally not recommended for full automation. Option B explicitly requires human oversight due to its sensitive nature. Therefore, the best fit for full or augmented automation for efficiency is the handling of routine, repeatable, and non-complex tasks.
(Reference: Google Cloud documentation on Gen AI adoption and efficiency states that Gen AI transforms work by automating repetitive and time-consuming tasks to free up time for strategic thinking and creativity.)

質問 # 74
An organization wants to quickly experiment with different Gemini models and parameters for content creation without a complex setup. What service should the organization use for this initial exploration?
  • A. Gemini for Google Workspace
  • B. Google AI Studio
  • C. Vertex AI Prediction
  • D. Vertex AI Studio
正解：D
解説：
The requirement is for a tool that facilitates quick experimentation with Gemini models and parameters without requiring significant technical setup, specifically targeting content creation (prompting/tuning) within the enterprise environment.
Vertex AI Studio (C) is the low-code, web-based UI component of Google Cloud's unified ML platform (Vertex AI). It is explicitly designed for non-technical users, developers, and data scientists to:
Quickly prototype and test different Foundation Models (including Gemini, Imagen, and Codey).
Experiment with model parameters (like Temperature, Top-P, and Max Output Tokens) through a user-friendly interface.
Refine prompts and set up initial tuning or grounding configurations before moving to large-scale production deployment.
Google AI Studio (A) is a very similar tool, but it's generally associated with non-enterprise/public prototyping for Google's models, whereas Vertex AI Studio is the enterprise-ready environment for Gen AI development on Google Cloud, which is the context of the exam.
Vertex AI Prediction (B) is the service for deploying and serving models for inference, not for initial experimentation.
Gemini for Google Workspace (D) is an application that uses Gen AI to boost productivity within apps like Docs and Gmail, but it does not provide the interface needed to experiment with models and tune parameters.
(Reference: Google Cloud documentation positions Vertex AI Studio as the low-code/no-code interface for rapidly prototyping, testing, and customizing Google's Foundation Models (like Gemini) before full production deployment.)

質問 # 75
......
Google事実が語るよりも説得力があることは明らかです。したがって、当社がコンパイルしたGenerative-AI-Leaderテストトレントを味わうために、このWebサイトで無料デモを用意しました。 弊社Pass4TestがまとめたGenerative-AI-Leader試験トレントは、試験に備えるための最高のGenerative-AI-Leader試験トレントであると私たちが確信している理由を理解するでしょう。 無料のデモはいつでも好きなときにダウンロードできます。いつでも試してみてください。Generative-AI-LeaderのGoogle Cloud Certified - Generative AI Leader Exam試験の資料は決してあなたを失望させません。
Generative-AI-Leader更新版: https://www.pass4test.jp/Generative-AI-Leader.html
P.S. Pass4TestがGoogle Driveで共有している無料かつ新しいGenerative-AI-Leaderダンプ：https://drive.google.com/open?id=14fNLDyfwSHebDuock8J0ODJLT6oesRfj
https://www.pdfvce.com
Reply

Use props Report

Return to List
High mode
B Color Image Link Quote Code Smilies
You need to log in before you can reply Login | Register

This forum Credits Rules

  •  

Service Hotline : (+86)18688117175
Contact us
E-mail : global@t-firefly.com
Copyright © 2014-2022, FIREFLY TECHNOLOGY CO.,LTD. All Rightts Reserved. 粤ICP备14022046号-2
Quick Reply Back to top Back to list