Generative-AI-Leaderの無料問題集（47題）、Generative-AI-Leader認定試験参考書

Google Generative-AI-Leader 問題集

試験コード：Generative-AI-Leader

試験名称：Google Cloud Certified - Generative AI Leader Exam

最近更新時間：2025-10-06

問題と解答：全47問

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質問 1：
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. Unsupervised learning
B. Reinforcement learning
C. Deep learning
D. Supervised learning
正解：B
解説: (Topexam メンバーにのみ表示されます)

質問 2：
The office of the CISO wants to use generative AI (gen AI) to help automate tasks like summarizing case information, researching threats, and taking actions like creating detection rules. What agent should they use?
A. Code agent
B. Customer service agent
C. Data agent
D. Security agent
正解：D
解説: (Topexam メンバーにのみ表示されます)

質問 3：
A highly regulated financial institution wants to use Gemini as the core decision engine for a loan approval system that will deterministically approve or reject loan applications based on a strict set of predefined criteria. Why is this an inappropriate use case for Gemini?
A. Gemini is designed for flexible content generation and inference, not rigid rule-based decisions.
B. Gemini deployment for this scenario would be too expensive and complex.
C. Gemini cannot integrate with required financial databases.
D. Gemini is not equipped to handle structured numerical data for financial assessments.
正解：A
解説: (Topexam メンバーにのみ表示されます)

質問 4：
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. Adjust the temperature parameter.
D. Use few-shot prompting.
正解：B
解説: (Topexam メンバーにのみ表示されます)

質問 5：
A development team is configuring a generative AI model for a customer-facing application and wants to ensure the generated content is appropriate and harmless. What is the primary function of the safety settings parameter in a generative AI model?
A. To determine the number of tokens the model can process at once by influencing the complexity and length of inputs and outputs.
B. To control the creativity and randomness of the model's output by adjusting the diversity of word choices.
C. To filter out potentially harmful or inappropriate content from the model's output based on the desired level of filtering.
D. To limit the maximum text length that the model generates by ensuring concise responses.
正解：C
解説: (Topexam メンバーにのみ表示されます)

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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	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 2	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 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.

参照：https://cloud.google.com/developers/generative-ai-leader

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