Open the auxiliary access

Firefly Open Source Community

   Login   |   Register   |
New_Topic
Firefly Open Source Community › Embedded Board › Face-RK3399› New Google Generative-AI-Leader Exam Labs, Testking ...
New Topic
Print Previous Topic Next Topic

[General] New Google Generative-AI-Leader Exam Labs, Testking Generative-AI-Leader Exam Qu

paulyou299

127

Credits

0

Prestige

0

Contribution

registered members

Rank: 2

Credits
127

【General】 New Google Generative-AI-Leader Exam Labs, Testking Generative-AI-Leader Exam Qu

Posted at yesterday 08:10      View：5 | Replies：0        Print      Only Author   [Copy Link] 1#
BONUS!!! Download part of Pass4sures Generative-AI-Leader dumps for free: https://drive.google.com/open?id=1kXzmnIINO32GOzDlJFP6uJIpXFxwXZ4V
You may want to know our different versions of Generative-AI-Leader exam questions. Firstly, PDF version is easy to read and print. Secondly software version simulates the real Generative-AI-Leader actual test guide, but it can only run on Windows operating system. Thirdly, online version supports for any electronic equipment and also supports offline use. For the first time, you need to open Generative-AI-Leader Exam Questions in online environment, and then you can use it offline. All in all, helping our candidates to pass the exam successfully is what we always looking for. Our Generative-AI-Leader actual test guide is your best choice.
Google Generative-AI-Leader Exam Syllabus Topics:
TopicDetails
Topic 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.
Topic 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.
Topic 3
  • 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.
Topic 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.

Testking Generative-AI-Leader Exam Questions - Generative-AI-Leader Accurate Study MaterialPass4sures Generative-AI-Leader exam dumps are audited by our certified subject matter experts and published authors for development. Generative-AI-Leader exam dumps are one of the highest quality Generative-AI-Leader Q&AS in the world. It covers nearly 96% real questions and answers, including the entire testing scope. Pass4sures guarantees you Pass Generative-AI-Leader Exam at first attempt.
Google Cloud Certified - Generative AI Leader Exam Sample Questions (Q38-Q43):NEW QUESTION # 38
What are core hardware components of the infrastructure layer in the generative AI landscape?
  • A. TPUs and GPUs
  • B. Pre-trained models
  • C. Tools and services for building AI models
  • D. User interfaces
Answer: A
Explanation:
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.)

NEW QUESTION # 39
A software development team wants to use generative AI (gen AI) to code faster so they can launch their software prototype quicker. What should the team do?
  • A. Use gen AI to suggest code snippets and complete functions.
  • B. Use gen AI to automatically generate comprehensive documentation for their code.
  • C. Use gen AI to identify potential bugs and security vulnerabilities in their code.
  • D. Use gen AI to refactor and optimize existing code.
Answer: A
Explanation:
While generative AI can assist with all the options listed (refactoring, documentation, bug identification), its most direct and significant impact on coding faster for a prototype is through code generation. Suggesting code snippets and completing functions directly accelerates the writing of new code, enabling quicker prototyping.
________________________________________

NEW QUESTION # 40
An organization is collecting data to train a generative AI model for customer service. They want to ensure security throughout the ML lifecycle. What is a critical consideration at this stage?
  • A. Applying the latest software patches to the AI model on a regular basis.
  • B. Implementing access controls and protecting sensitive information within the training data.
  • C. Monitoring the AI model's performance for unexpected outputs and potential errors.
  • D. Establishing ethical guidelines for AI model responses to ensure fairness and avoid harm.
Answer: B
Explanation:
The stage mentioned is Data Collection/Training Data Preparation. In the machine learning lifecycle, this initial stage is where raw data is ingested and processed. If the model is being trained for customer service, the data (e.g., customer transcripts) is highly likely to contain sensitive information (like Personally Identifiable Information or PII).
Therefore, the most critical security and privacy consideration at this stage is protecting the integrity and confidentiality of the data itself.
Implementing strong access controls and protecting sensitive information (A) is the essential first step in a secure AI pipeline, aligning with Google's Secure AI Framework (SAIF). If data access is not controlled and sensitive data is not de-identified or redacted before it is used for training, the resulting model could leak that sensitive information to users.
Options B, C, and D are all important controls, but they occur at later stages of the ML lifecycle:
B (Software patches/latest versions) is part of deployment and management.
C (Ethical guidelines/fairness) is a Responsible AI goal implemented via guardrails and testing (later stages).
D (Monitoring) is an MLOps step that happens after deployment.
The critical consideration at the data collection stage is ensuring the data's security and privacy before it influences the model.
(Reference: Google Cloud guidance on securing generative AI emphasizes that one of the most significant risks is data leakage, making safeguarding training data and implementing identity and access control the foundational steps in the data ingestion and preparation phases.)

NEW QUESTION # 41
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?
  • A. Reinforcement learning
  • B. Supervised learning
  • C. Unsupervised learning
  • D. Deep learning
Answer: A
Explanation:
This scenario perfectly describes reinforcement learning. In reinforcement learning, an agent learns to make decisions by interacting with an environment, receiving1 rewards for desirable actions and penalties for undesirable ones,2 and iteratively improving its behavior through trial and error to maximize cumulative reward.
________________________________________

NEW QUESTION # 42
A large e-commerce company with a substantial product catalog and many support documents has customers struggling to find information on their website. This leads to high support costs and poor user experience. The company wants a Google Cloud solution to improve website search and reduce support costs while improving customer satisfaction. What Google Cloud product should the company use?
  • A. Vertex AI Platform
  • B. Google Shopping
  • C. Google Search
  • D. Vertex AI Search
Answer: D
Explanation:
Vertex AI Search is ideal for this scenario. It allows companies to build sophisticated search experiences over their own product catalogs and support documents. This improves accuracy and helps customers find what they need, directly addressing high support costs and poor user experience. Vertex AI Platform is broader for general ML development, Google Shopping is for consumers, and Google Search is for the public web.
________________________________________

NEW QUESTION # 43
......
Our Generative-AI-Leader training materials are the latest, valid and accurate study material for candidates who are eager to clear Generative-AI-Leader exams. You can actually grasp the shortest time to do as much interesting and effective things you like as possible. Generative-AI-Leader real questions are high value & high pass rate with competitive price products. And our pass rate of Generative-AI-Leader Study Guide is as high as 99% to 100%. As long as you study with our Generative-AI-Leader exam questions, you will pass the Generative-AI-Leader exam easily.
Testking Generative-AI-Leader Exam Questions: https://www.pass4sures.top/Google-Cloud-Certified/Generative-AI-Leader-testking-braindumps.html
BTW, DOWNLOAD part of Pass4sures Generative-AI-Leader dumps from Cloud Storage: https://drive.google.com/open?id=1kXzmnIINO32GOzDlJFP6uJIpXFxwXZ4V
https://www.lead2passed.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