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| Section | Objectives |
|---|---|
| Topic 1: Google Cloud Generative AI Products and Tools | - Prompt design and prompt engineering tools - AI APIs and model deployment options on Google Cloud - Vertex AI and Gemini models overview |
| Topic 2: Business Applications and Adoption Strategy | - AI-driven transformation and workflow integration - Identifying business use cases for generative AI - Measuring ROI and value of generative AI initiatives |
| Topic 3: 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 |
| Topic 4: Responsible AI and Governance | - AI safety, bias, and fairness considerations - Responsible AI principles and compliance - Data privacy and security in generative AI systems |
>> Generative-AI-Leader基礎訓練 <<
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質問 # 59
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?
正解：A
解説：
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.)
質問 # 60
An organization wants granular control over who can use and see their generative AI models and related resources on Google Cloud. Which Google Cloud security offering is specifically for this purpose?
正解：D
解説：
Identity and Access Management (IAM) is the fundamental Google Cloud service that allows you to define who has what access to which resources. It provides granular control over permissions for users, groups, and service accounts, including access to generative AI models and related data.
質問 # 61
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?
正解：B
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.
質問 # 62
SummitCart, a global e commerce fulfillment company, is deploying a generative AI driven system in its regional distribution centers to observe conveyor operations and forecast sorter and motor failures in real time. Any outage would pause order packing and could cost several million dollars per hour. When choosing the model and the managed platform, which characteristic should be prioritized for this mission critical rollout?
正解：A
解説：
This rollout is mission critical and any downtime would incur enormous costs, so the platform and model selection must prioritize guaranteed uptime.
For an always-on operational system in distribution centers you need high availability commitments that are explicit and enforceable. A documented uptime SLA signals that the provider designs and operates the service for reliability and that it will be supported with measurable objectives and remediation if targets are missed. Choosing services that publish clear availability targets and provide regional resilience, failover capabilities, and enterprise support reduces the risk of production outages and protects revenue.
質問 # 63
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?
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 # 64
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