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| Section | Objectives |
|---|---|
| Fundamentals of Generative AI | - Key use cases and limitations of generative AI - Core concepts of generative AI and large language models - Difference between traditional AI, machine learning, and generative AI |
| Responsible AI and Governance | - Data privacy and security in generative AI systems - AI safety, bias, and fairness considerations - Responsible AI principles and compliance |
| 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 |
| 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 |
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NEW QUESTION # 59
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?
Answer: C
Explanation:
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.
NEW QUESTION # 60
What is the function of the platform layer in the generative AI (gen AI) landscape?
Answer: D
Explanation:
The platform layer supplies the development environment and operational tools required to build, customize, deploy, and manage generative AI solutions. It connects foundation models and infrastructure with developers and organizations creating applications. Google Cloud's Vertex AI is an example: it provides access to models, prompt-design tools, tuning capabilities, evaluation services, deployment facilities, and model- management functions. Option A describes the application layer, where end users directly consume AI capabilities. Option B more narrowly describes access to the model layer rather than the complete function of a platform. Option D describes the infrastructure layer, which supplies processors, storage, networking, and other computational resources. Therefore, providing tools for interacting with models and building and deploying AI solutions is the platform layer's defining function.
NEW QUESTION # 61
An engineering team at example.com spends about 12 hours each week producing boilerplate and scaffolding for routine service endpoints, and they want an AI capability that can generate this repetitive code from concise requirements or from established patterns so the developers can concentrate on complex tasks. What primary use of generative AI does this scenario represent?
Answer: D
Explanation:
The scenario describes generating boilerplate and scaffolding from concise requirements or established patterns so developers can focus on more complex work.
This use case aligns with generative AI that produces source code from prompts or patterns. It automates repetitive implementation details, accelerates service setup, and helps teams reduce time spent on routine endpoint creation while maintaining consistency.
NEW QUESTION # 62
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?
Answer: C
Explanation:
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.
NEW QUESTION # 63
A company is defining their generative AI strategy. They want to follow Google-recommended practices to increase their chances of success. Which strategy should they use?
Answer: A
Explanation:
Google Cloud often recommends a "top-down" approach for generative AI strategy. This means starting with clear business objectives and leadership alignment on how generative AI can solve critical business problems, rather than simply experimenting from the bottom up without a clear strategic direction.
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NEW QUESTION # 64
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