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：2026-07-28

問題と解答：全103問

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質問 1：
A customer support team uses a generative AI agent to answer product questions. The AI cannot answer questions about a new software version with features and troubleshooting documented in internal articles that weren ' t part of its training data. What cost-effective technique should they use to improve their output?
A. Basic prompting with provided examples
B. Retrieval augmented generation (RAG)
C. Fine-tuning the foundation model with the new documentation
D. Role-based prompting
正解：B
解説: (Topexam メンバーにのみ表示されます)

質問 2：
What is the function of the platform layer in the generative AI (gen AI) landscape?
A. To provide tools for users to interact with gen AI models, and build and deploy AI solutions.
B. To provide the computational resources needed to train and run generative AI models.
C. To allow access to pre-trained gen AI models.
D. To allow users to interact with and leverage AI capabilities.
正解：A
解説: (Topexam メンバーにのみ表示されます)

質問 3：
What will Google Cloud ' s Agent Assist help a company achieve?
A. The ability to build and deploy deterministic and generative chatbot agents for automated customer support.
B. The ability to provide real-time assistance and recommended responses to live customer service agents during their interactions.
C. The infrastructure to provide an enterprise-grade contact center solution with omnichannel support, routing, and integration with CRM systems.
D. The ability to analyze conversational data to identify customer sentiment, common topics of discussion, and insights into agent performance and customer experience.
正解：B
解説: (Topexam メンバーにのみ表示されます)

質問 4：
An organization is increasingly concerned about the security of its sensitive business data as it begins to use generative AI applications that are hosted on Google Cloud. They want to ensure that the underlying infrastructure itself has robust security measures built in from the ground up to protect against potential threats and vulnerabilities. What Google Cloud security features directly address this need?
A. Identity and Access Management
B. Security Command Center
C. Google Cloud Observability
D. Secure-by-design infrastructure
正解：D
解説: (Topexam メンバーにのみ表示されます)

質問 5：
A user asks a generative AI model about the scientific accuracy of a popular science fiction movie. The model confidently states that humans can indeed travel faster than light, referencing specific but entirely fictional theories and providing made-up explanations of how this is achieved according to the movie ' s " established science. " The model presents this information as factual, without indicating that it originates from a fictional work. What type of model limitation is this?
A. Knowledge cutoff
B. Hallucination
C. Bias
D. Data dependency
正解：B
解説: (Topexam メンバーにのみ表示されます)

質問 6：
A large online retailer with a vast product catalog wants to improve customer satisfaction by making it easier for shoppers to find the specific products they ' re looking for. The retailer also wants to provide personalized recommendations to increase sales. What should the company do?
A. Use Recommendations to provide custom recommendations to users.
B. Use Agent Search on Gemini Enterprise Agent Platform to allow employees to search across the company ' s intranet and receive personalized search results based on their role, past searches, and current collaborators.
C. Use AI Commerce Search on Gemini Enterprise for Customer Experience to enable natural language searches, provide custom recommendations, and improve product discovery.
D. Use Google Cloud ' s Vision API to analyze product images, automatically tag the images with relevant keywords, and improve search accuracy.
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	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 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.
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	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.

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

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