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| Section | Objectives |
|---|---|
| Topic 1: 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 2: Business Applications and Adoption Strategy | - AI-driven transformation and workflow integration - Measuring ROI and value of generative AI initiatives - Identifying business use cases for generative AI |
| Topic 3: Google Cloud Generative AI Products and Tools | - AI APIs and model deployment options on Google Cloud - Vertex AI and Gemini models overview - Prompt design and prompt engineering tools |
| Topic 4: Responsible AI and Governance | - AI safety, bias, and fairness considerations - Data privacy and security in generative AI systems - Responsible AI principles and compliance |
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質問 # 65
An organization is building a generative AI agent for employee travel bookings. The agent needs to connect to external flight and hotel systems for availability, pricing, and reservations. They implement the most simple and effective way for the agent to interact with the external travel provider system, following Google Cloud- recommended practices. What method should they use?
正解：C
解説：
Extensions provide a standardized mechanism through which an AI agent can invoke external APIs and complete real-world actions. A travel extension can connect the agent to flight and hotel services, pass structured parameters, retrieve current availability and prices, and submit reservations. This is more maintainable and consistent than embedding separate custom functions directly inside the agent. Pre-loaded data stores cannot satisfy the requirement because availability and pricing change continuously, while reservations require transactional access to the provider's live system. "Plugins" is not the recommended Google Cloud mechanism identified for this agent-integration scenario. Extensions enable agents to move beyond answering questions by securely communicating with external services through defined interfaces.
Consequently, they are the simplest and most effective Google-recommended method for integrating the travel agent with provider APIs.
質問 # 66
A company wants to use an AI agent to automate some tasks. They want everyone to understand the different functions of an AI agent. What is the function of an AI agent in the context of gen AI?
正解：B
解説：
An AI agent, especially in the context of generative AI, is designed to be more autonomous and capable than a simple model. Its function is to understand a goal, analyze a situation, leverage various tools (including other generative AI models or external APIs), and make decisions or take actions to achieve that goal, often with minimal human intervention.
質問 # 67
A project team is developing a generative AI application that needs to process and summarize lengthy documents. They are considering the limitations of the underlying language model. What is a key consideration regarding the token count for this application?
正解：D
解説：
Every large language model processes input and generates output in units called tokens. A model's context window defines its maximum token capacity. If an input prompt-along with the lengthy documents provided within it-exceeds this context window limit, the model will either reject the request or truncate the text, failing to process the complete document context. Options B, C, and D are incorrect because token count is an architectural and computational measure of data volume/capacity, not a control for creativity, processing power reduction, or content safety filtering.
質問 # 68
A large enterprise company is experiencing challenges managing their model. They have many versions of their model, including the code, data, and parameters used to train the models. They need to find a solution to manage versions, track changes, and stay organized throughout their lifecycle. What Gemini Enterprise Agent Platform tool should the company use?
正解：A
解説：
Model Registry provides a centralized repository for organizing and governing machine learning models and their versions throughout the model lifecycle. It allows teams to register models, retain version history, associate metadata, track lineage and deployment status, and consistently identify which model artifact is approved or operating in an environment. These capabilities directly address the company's need to manage numerous versions and remain organized. Model Monitoring observes deployed model behavior, including performance changes and data drift, but is not the primary version-management repository. Pipelines automate repeatable machine learning workflows such as training, evaluation, and deployment. Feature Store manages reusable machine learning features rather than complete model versions and their lifecycle metadata.
Consequently, Model Registry is the appropriate tool for tracking, organizing, and controlling the company's evolving model assets.
質問 # 69
A consumer electronics manufacturer is selecting a cloud platform to support an eight to twelve year roadmap for generative AI. Executives want a provider recognized for foundational AI breakthroughs that quickly become integrated services and purpose-built infrastructure. Which inherent strength of Google Cloud best aligns with these goals?
正解：C
解説：
This choice aligns with an eight to twelve year generative AI roadmap because Google consistently turns cutting edge research into widely available capabilities. Breakthroughs from Google Research become integrated services in Google Cloud such as managed model training, tuning, and deployment on Vertex AI. The company also builds purpose built infrastructure like Cloud TPU that is engineered for large scale training and inference. This pattern of research leadership that rapidly becomes productized gives organizations confidence that future advances in models, tooling, and hardware will arrive as usable cloud services.
質問 # 70
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