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| Section | Weight | Objectives |
|---|---|---|
| Business Strategies for Successful Generative AI Solutions | 15% | - Identifying business use cases and value opportunities - Planning and adoption frameworks - Scaling and measuring success of generative AI initiatives - Governance, risk management, and compliance |
| Fundamentals of Generative AI | 30% | - Foundation models: definition, capabilities, and use cases - Key technologies and differences from traditional AI - Responsible AI principles and application - Core concepts and characteristics of generative AI |
| Google Cloud's Generative AI Offerings | 35% | - Model Garden and available models - Generative AI application development platforms - Enterprise integration and security features - Overview of Google Cloud generative AI services and tools - Vertex AI generative AI capabilities |
| Techniques to Improve Generative AI Model Output | 20% | - Evaluation and optimization of output quality - Fine-tuning and adaptation methods - Mitigation of bias and inaccuracies - Prompt engineering principles and best practices |
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NEW QUESTION # 14
What are core hardware components of the infrastructure layer in the generative AI landscape?
Answer: D
Explanation:
The Generative AI landscape is often broken down into several functional layers: Applications, Agents, Platforms, Models, and Infrastructure.
The Infrastructure Layer is the foundation, providing the physical and virtual computing resources necessary to run and train the large models. These resources include servers, storage, networking, and most importantly, the specialized hardware accelerators required for high-volume, parallel computation.
The core hardware components are the Graphics Processing Units (GPUs) and the custom-designed Tensor Processing Units (TPUs) (A). These accelerators are optimized for the massive matrix operations fundamental to deep learning and Gen AI model training and inference.
Options B (User interfaces) and D (Tools and services) refer to the Application and Platform layers, respectively.
Option C (Pre-trained models) refers to the Model layer.
The physical hardware underpinning these abstract layers are the TPUs and GPUs.
(Reference: Google Cloud Generative AI Study Guides state that the Infrastructure Layer provides the core computing resources needed for generative AI, including the physical hardware (like servers, GPUs, and TPUs) and the essential software needed to train, store, and run AI models.)
NEW QUESTION # 15
A company is exploring Google Agentspace to improve how its employees search for information on their enterprise systems and automate certain tasks. What is the key business advantage of using Agentspace?
Answer: C
Explanation:
Google Agentspace (or similar agent platforms) is designed to empower employees with AI-powered assistants that can navigate and interact with enterprise systems, analyze documents, and automate tasks. This directly leads to improved employee productivity and more efficient data interaction by leveraging AI to streamline workflows and provide faster access to information.
NEW QUESTION # 16
A human resources team is implementing a new generative AI application to assist the department in screening a large volume of job applications. They want to ensure fairness and build trust with potential candidates. What should the team prioritize?
Answer: C
Explanation:
To ensure fairness and build trust, especially in sensitive areas like job applications, transparency in how AI evaluates applications and uses data is paramount. This involves understanding potential biases, explaining decisions (where possible), and ensuring human oversight.
NEW QUESTION # 17
A development team is building an internal knowledge base chatbot to answer employee questions about company policies and procedures. This information is stored across various documents in Google Cloud Storage and is updated regularly by different departments. What is the primary benefit of using Google Cloud's RAG APIs in this scenario?
Answer: D
Explanation:
The primary benefit of RAG (Retrieval-Augmented Generation) in this context is its ability to ensure the chatbot provides accurate and up-to-date information. By retrieving relevant and recent policy documents from Cloud Storage in real-time and then grounding the LLM's response with this information, the chatbot avoids hallucinating or providing outdated answers, which is crucial for an internal knowledge base.
________________________________________
NEW QUESTION # 18
Summit Dynamics has three groups that plan to use generative AI on Google Cloud. The Innovation Lab requires complete control of the guest operating system and exact NVIDIA driver builds on virtual machines so they can trial cutting edge AI frameworks. The Product Engineering group wants to write only Python code for a custom model while Google manages the operating system, autoscaling, and infrastructure. The Communications department wants a ready to use assistant that helps them compose emails inside their current Workspace apps with no coding.
Which combination of Google Cloud services correctly aligns to the IaaS, PaaS, and SaaS models for these groups?
Answer: C
Explanation:
The Innovation Lab needs full control of the guest operating system and precise NVIDIA driver versions on virtual machines. Compute Engine provides raw VM instances in an infrastructure as a service model so the team can pick images, install and pin specific drivers, and manage the OS as required.
The Product Engineering group wants to write only Python while Google manages the operating system, autoscaling, and infrastructure. Vertex AI fits this platform as a service need because it provides managed training and serving, serverless endpoints, and autoscaling so developers can focus on code and models rather than machines.
The Communications department wants a ready to use assistant in existing Workspace apps with no coding. Gemini for Workspace is software as a service that integrates into Gmail, Docs, and other Workspace apps and delivers assistance without any infrastructure setup.
NEW QUESTION # 19
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