Generative-AI-Leader教科書の決定版Google試験合格確定

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Google Generative-AI-Leader Exam Syllabus Topics:

Section	Objectives
Fundamentals of Generative AI	- Core concepts of generative AI and large language models - Difference between traditional AI, machine learning, and generative AI - Key use cases and limitations of generative AI
Google Cloud Generative AI Products and Tools	- Prompt design and prompt engineering tools - Vertex AI and Gemini models overview - AI APIs and model deployment options on Google Cloud
Business Applications and Adoption Strategy	- Measuring ROI and value of generative AI initiatives - Identifying business use cases for generative AI - AI-driven transformation and workflow integration
Responsible AI and Governance	- AI safety, bias, and fairness considerations - Responsible AI principles and compliance - Data privacy and security in generative AI systems

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Google Cloud Certified - Generative AI Leader Exam 認定 Generative-AI-Leader 試験問題 (Q100-Q105):

質問 # 100
A company is trying to decide which platform to use to optimize its generative AI (gen AI) solutions. Why should the company use Vertex AI Platform?

A. It provides gen AI coding assistance with enterprise security and privacy protection.
B. It provides scalable and cost-effective object storage for data used in machine learning workflows.
C. It provides a unified platform of tools for building, deploying, and managing machine learning.
D. It provides a mechanism for efficient analysis and exploration of large datasets used in machine learning.

正解：C

解説：
Vertex AI is Google Cloud's core, end-to-end Machine Learning Operations (MLOps) platform, designed to cover the entire ML lifecycle.
The key benefit of Vertex AI, particularly for generative AI, is that it provides a unified platform (D) where all stages of AI development-from accessing foundation models in Model Garden, testing in Vertex AI Studio, training and tuning (via tools like Reinforcement Learning from Human Feedback), to deploying, and monitoring models in production-can be managed from a single service. This significantly reduces complexity, improves collaboration between teams (data scientists, engineers, business leaders), and ensures enterprise-grade governance and scalability necessary for production Gen AI solutions.
Option A describes BigQuery.
Option B describes Gemini Code Assist.
Option C describes Cloud Storage.
Vertex AI is the overarching platform that integrates all these tools to deliver a streamlined MLOps workflow.
(Reference: Google Cloud documentation states that Vertex AI is the unified AI development platform that brings together Google Cloud services for building, deploying, and managing machine learning models and generative AI solutions.)

質問 # 101
A company is developing a conversational AI chatbot. They need to ensure the chatbot can engage in human-like conversations and provide accurate information. What should they do to enhance the chatbot's ability to understand and respond effectively to user prompts?

A. Lower model temperature setting to produce more consistent and predictable responses.
B. Limit the chatbot's training data to prevent it from learning irrelevant information.
C. Use strict keyword matching to ensure that the chatbot only responds to specific commands.
D. Use prompt engineering techniques, like few-shot prompting, to provide the chatbot with examples of successful interactions.

正解：D

解説：
Prompt engineering, especially techniques like few-shot prompting (providing examples of desired input-output pairs), is crucial for guiding a generative AI model to understand context and generate relevant, human-like responses. Limiting data or using strict keyword matching would severely restrict the chatbot's conversational ability, and lowering temperature makes responses less creative, not necessarily more understanding.

質問 # 102
A company's development team is eager to start building generative AI solutions with Google Cloud, but has limited experience in AI development. They need to launch their gen AI solution quickly. What Google Cloud benefit would help the company achieve their goal?

A. Google Cloud's focus on continuous improvement provides access to the latest AI tools, features, and best practices.
B. Google Cloud's collaborative AI community and support forums connect developers with AI experts.
C. Google Cloud's pre-trained models and low- and no-code AI tools and services.
D. Google Cloud's comprehensive training materials and tutorials to help developers.

正解：C

解説：
For a team with limited AI experience needing to launch quickly, leveraging pre-trained models (foundation models) and low-code/no-code tools significantly reduces the development burden and accelerates time to market. This allows them to build and deploy generative AI solutions without requiring deep expertise from scratch. While other options are helpful, this directly addresses the need for quick launch with limited experience.
________________________________________

質問 # 103
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?

A. Use prompt chaining.
B. Use role prompting.
C. Use few-shot prompting.
D. Adjust the temperature parameter.

正解：B

解説：
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.
________________________________________

質問 # 104
What are core hardware components of the infrastructure layer in the generative AI landscape?

A. User interfaces
B. TPUs and GPUs
C. Pre-trained models
D. Tools and services for building AI models

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.

質問 # 105
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