Generative-AI-Leader最新考古題，Generative-AI-Leader考試資料

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Google Generative-AI-Leader Exam Overview:

Certification Vendor:Google Cloud
Exam Name:Generative AI Leader Certification Exam
Exam Number:Generative-AI-Leader
Passing Score:Not publicly disclosed
Real Exam Qty:50-60
Available Languages:Japanese, Portuguese, English, Spanish
Exam Duration:90 minutes
Exam Price:USD 99 (plus tax where applicable)
Certificate Validity Period:3 years
Exam Format:Multiple choice
Recommended Training:Generative AI Leader Training Course
Generative AI Leader Study Guide
Exam Registration:Google Cloud Certification Registration
Sample Questions:Google Generative-AI-Leader Sample Questions
Exam Way:Online-proctored or onsite-proctored
Pre Condition:No prerequisites required; open to all roles and backgrounds
Official Syllabus URL:https://cloud.google.com/learn/certification/generative-ai-leader

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Google Generative-AI-Leader 考試大綱：

主題簡介
主題 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.
主題 2
  • 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.
主題 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.
主題 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.

最新的 Google Cloud Certified Generative-AI-Leader 免費考試真題 (Q18-Q23):

問題 #18
A large e-commerce company has a vast catalog of product images and needs to classify these images to improve product categorization and search functionality on their website. Most of the images in their dataset are labeled. They want to build and train an image recognition model for their product catalog. Which Google Cloud offering should they use?

答案：C

解題說明：
AutoML is appropriate because the company possesses a largely labeled image dataset and wants to train a customized image-classification model. AutoML simplifies model development by automating important activities such as architecture selection, training, tuning, and evaluation, reducing the amount of specialist machine learning code required. The labeled product images provide the input examples and target categories needed for supervised image classification. Gemini Code Assist supports software development and does not train image-recognition models. Google AI Studio is primarily used to prototype applications and prompts with Gemini models rather than create a dedicated supervised classifier from a labeled image collection. Agent Search focuses on retrieving enterprise information and is unrelated to image-model training. Therefore, AutoML on Gemini Enterprise Agent Platform is the offering that best satisfies the custom image-recognition requirement.


問題 #19
A global news agency is developing a generative AI tool to quickly summarize breaking news articles 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?

答案：D

解題說明：
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.
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問題 #20
What is the function of the platform layer in the generative AI (gen AI) landscape?

答案：A

解題說明：
The platform layer supplies the development environment and operational tools required to build, customize, deploy, and manage generative AI solutions. It connects foundation models and infrastructure with developers and organizations creating applications. Google Cloud's Vertex AI is an example: it provides access to models, prompt-design tools, tuning capabilities, evaluation services, deployment facilities, and model- management functions. Option A describes the application layer, where end users directly consume AI capabilities. Option B more narrowly describes access to the model layer rather than the complete function of a platform. Option D describes the infrastructure layer, which supplies processors, storage, networking, and other computational resources. Therefore, providing tools for interacting with models and building and deploying AI solutions is the platform layer's defining function.


問題 #21
An organization wants to use generative AI to create a marketing campaign. They need to ensure that the AI model generates text that is appropriate for the target audience. What should the organization do?

答案：C

解題說明：
Role prompting is a technique where you instruct the generative AI model to "act as" a specific persona or character. By assigning the model a role (e.g., "Act as a marketing expert writing for a young, tech-savvy audience"), you can guide its tone, style, and content to be appropriate for the target audience of the marketing campaign.
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問題 #22
What is the function of the platform layer in the generative AI (gen AI) landscape?

答案：A


問題 #23
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