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	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 3	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 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.

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

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Google Cloud Certified - Generative AI Leader 認定 Generative-AI-Leader 試験問題：

問題 #1
A retail company with a large online catalog wants to improve customer experience and drive sales by implementing multimodal search capabilities (image, voice, and text). What is a primary business benefit of this capability?

A. Improved customer engagement and product discovery leading to increased satisfaction and potential sales.
B. Lowered operational costs associated with managing and updating product information across different platforms and channels.
C. Streamlined inventory management processes and more accurate demand forecasting for popular items.
D. Reduced dependency on keyword optimization for product listings and improved search engine rankings.

問題 #2
What is the continuous cycle of observing, interpreting, planning, and acting that makes up an AI agent's "thinking process"?

A. User interface
B. Reasoning loop
C. Platform integration
D. Model training

問題 #3
A team is using a generative AI model to automatically generate short summaries of customer feedback. They need to ensure that these summaries are concise and easy to digest. What model setting should they adjust?

A. Output length
B. Safety settings
C. Temperature
D. Top-p (nucleus sampling)

問題 #4
A data science team needs a centralized and organized location to store its various model versions, track their metadata, and easily deploy them to the respective applications. What Google Cloud service should they use?

A. Cloud Storage
B. BigQuery
C. Vertex AI Pipelines
D. Model Registry

問題 #5
A pharmaceutical company's research and development department spends significant time manually reviewing new scientific papers to identify potential drug targets. They need a solution that can answer questions about these documents and provide summarized insights to researchers without requiring extensive coding expertise. What should the organization do?

A. Use Gemini for Google Workspace to facilitate collaborative document review.
B. Use Vertex AI AutoML to train a model that classifies papers into predefined research areas.
C. Use Vertex AI Agent Builder to create a custom AI agent.
D. Use Vertex AI Search to index the papers and enable keyword-based searches.

解説：

問題 #1
正解： A

問題 #2
正解： B

問題 #3
正解： A

問題 #4
正解： D

問題 #5
正解： C

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Google Generative-AI-Leader 認定試験の出題範囲：

Google Cloud Certified - Generative AI Leader 認定 Generative-AI-Leader 試験問題：

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