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NEW QUESTION # 13
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: D
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 # 14
A market research firm wants to use a Google Cloud prebuilt generative AI offering to streamline the process of extracting and synthesizing information from lengthy market reports and research papers. Their goal is to improve efficiency and provide faster insights to their clients. What should the organization do?
Answer: B
Explanation:
NotebookLM is a prebuilt, source-grounded research and synthesis tool designed for working with uploaded documents and other selected sources. The firm can provide its market reports and research papers, ask questions about their contents, generate summaries, compare information, and identify important themes. Because responses are grounded in the supplied sources, researchers can obtain focused insights more efficiently while retaining the ability to review supporting material. The standard Gemini app can provide general assistance but is less specifically optimized for a defined document collection. Building custom conversational agents would introduce unnecessary development effort when a prebuilt offering satisfies the requirement. Gemini in Google Workspace can assist with drafting and collaboration, but the central need is extracting and synthesizing information from lengthy source documents. NotebookLM is therefore the most appropriate choice.
NEW QUESTION # 15
What is a key advantage of using Google's custom-designed TPUs?
Answer: D
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.
NEW QUESTION # 16
An organization is deploying generative AI applications on Google Cloud. They want to understand the fundamental security measures integrated into Google ' s global network and hardware. What term specifically describes these inherent protective measures?
Answer: B
Explanation:
Secure-by-design infrastructure means that security protections are incorporated into Google Cloud's hardware, network, and software architecture from their initial design rather than being added only after deployment. Google's infrastructure applies layered controls such as custom hardware security, encryption, identity-based access, network protections, continuous monitoring, and a hardened global architecture. These measures protect workloads and data at multiple levels and provide a secure foundation for generative AI applications. Network protection describes only one part of this broader architecture. Operations management concerns administration and reliability processes, while security oversight refers generally to governance and supervision rather than inherent technical controls. Because the organization is asking for the term covering security measures fundamentally integrated into Google's global network and hardware, secure-by-design infrastructure is the precise and comprehensive answer.
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: B
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
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