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| Section | Objectives |
|---|---|
| Google Cloud Generative AI Products and Tools | - Vertex AI and Gemini models overview - AI APIs and model deployment options on Google Cloud - Prompt design and prompt engineering tools |
| 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 | - Responsible AI principles and compliance - AI safety, bias, and fairness considerations - Data privacy and security in generative AI systems |
| Business Applications and Adoption Strategy | - Measuring ROI and value of generative AI initiatives - AI-driven transformation and workflow integration - Identifying business use cases for generative AI |
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NEW QUESTION # 59
What are core hardware components of the infrastructure layer in the generative AI landscape?
Answer: A
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.
NEW QUESTION # 60
A company is developing a conversational AI chatbot. They need to ensure the chatbot can engage in human-like conversations and provide accurate information. What should they do to enhance the chatbot's ability to understand and respond effectively to user prompts?
Answer: B
Explanation:
Prompt engineering, especially techniques like few-shot prompting (providing examples of desired input-output pairs), is crucial for guiding a generative AI model to understand context and generate relevant, human-like responses. Limiting data or using strict keyword matching would severely restrict the chatbot's conversational ability, and lowering temperature makes responses less creative, not necessarily more understanding.
NEW QUESTION # 61
A team is discussing the different layers of the generative AI (gen AI) landscape and where specific tools and technologies fit within those layers. They want to clarify the role of Gemini Enterprise Agent Platform and data management tools in the overall landscape. In which layer of the generative AI landscape are Gemini Enterprise Agent Platform and data management tools?
Answer: C
Explanation:
The generative AI technology stack consists of four key layers:
Infrastructure: Compute (TPUs, GPUs, Compute Engine), networking, and physical infrastructure.
Models: Foundation models (Gemini, Gemma, Imagen, third-party models in Model Garden).
Platform: Development and management software suites-including Gemini Enterprise Agent Platform (formerly Vertex AI), Vertex AI Studio, Model Registry, and data management/grounding services-used to build, tune, evaluate, and orchestrate workloads.
Agents / Applications: End-user conversational agents, copilots, and business applications delivering specific capabilities.
NEW QUESTION # 62
What is a key advantage of using Google's custom-designed TPUs?
Answer: A
Explanation:
TPUs (Tensor Processing Units) are custom-designed hardware accelerators developed by Google specifically for high-performance machine learning tasks. Their advantage lies in their architecture, which is optimized for the massively parallel matrix multiplication operations that form the mathematical backbone of deep learning and large language models (LLMs).
TPUs excel at parallel processing (C) for training and running machine learning workloads, allowing computations to be performed simultaneously across numerous cores. This makes them significantly faster and more efficient than traditional CPUs or even general-purpose GPUs for tasks like training massive generative models (e.g., Gemini).
TPUs are a core component of the Infrastructure Layer in the Generative AI landscape, providing the foundational compute resources.
While Google offers very small, specialized TPUs for the edge (like Edge TPU), the primary, large-scale advantage is in the cloud for accelerating training and inference for complex ML models.
Options A describes the Edge TPU or Gemini Nano deployment strategy, not the general, key advantage. Options B and D misrepresent the function, as TPUs are compute hardware, not storage accelerators or general-purpose CPU replacements.
(Reference: Google's training materials on the Generative AI Infrastructure Layer explicitly list TPUs and GPUs as the physical hardware components providing the core computing resources needed for generative AI, with TPUs being specialized for accelerating ML workloads and parallel processing.)
NEW QUESTION # 63
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?
Answer: B
Explanation:
The requirement is to answer questions about the documents and provide summarized insights without requiring extensive coding expertise. Vertex AI Agent Builder is designed precisely for creating custom AI agents, often with low-code or no-code capabilities, that can interact with and process large volumes of information like scientific papers. While Vertex AI Search could index papers for keyword searches, it doesn't directly answer questions or provide summarized insights in the same way a generative AI agent built with Agent Builder could. Gemini for Google Workspace is for collaborative work, not specifically for building custom AI agents for document analysis. Vertex AI AutoML is for training classification models, which is different from answering questions and summarizing.
NEW QUESTION # 64
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