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| Section | Objectives |
|---|---|
| Topic 1: AI Infrastructure Design | - Networking for AI workloads - Storage and data pipeline design - Compute and GPU considerations |
| Topic 2: Security in Private Cloud AI | - Identity and access management - Workload and data protection |
| Topic 3: HPE Private Cloud AI Fundamentals | - Overview of private cloud AI concepts - Core AI workload characteristics |
| Topic 4: Data Management and Governance | - Data governance and compliance - Data lifecycle management |
| Topic 5: HPE GreenLake for AI Solutions | - Consumption-based IT model for AI - GreenLake architecture and services |
| Topic 6: Deployment and Operations | - Deployment models for AI solutions - Monitoring and optimization - Lifecycle management of AI infrastructure |
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NEW QUESTION # 51
A global logistics company is designing an enterprise-grade AI solution. The project has two main goals:
1. Goal 1: Develop a highly accurate, proprietary logistics optimization model by fine-tuning a foundation model on the company's massive, confidential shipping dataset (20TB). This requires a secure, high- performance, multi-node training environment.
2. Goal 2: Deploy a generative AI-powered chatbot for the customer service department. The chatbot must provide real-time shipment status and answer policy questions based on a knowledge base that is updated hourly.
The company is classified as an 'AI Pro,' with a formal AI strategy and a Center of Excellence, but they want a turnkey solution to accelerate time-to-market.
Which components and strategies should the architect propose to meet all the customer's requirements?
(Select all that apply.)
```
Customer Profile:
- Industry: Global Logistics
- AI Maturity: AI Pro
- Key Workloads: Large-scale Fine-Tuning, Real-time RAG
- Desired Solution: Turnkey, enterprise-grade private cloud
```
Answer: B,D,F
NEW QUESTION # 52
What is the primary architectural advantage of the NVIDIA Grace Hopper Superchip (e.g., GH200) for large-scale AI workloads?
Answer: D
NEW QUESTION # 53
A customer is building a large-scale AI training cluster using HPE Private Cloud AI.
They have two main requirements for the storage solution:
1. It must provide extremely high throughput and low latency to feed data to a large cluster of NVIDIA H100 GPUs.
2. It must be able to scale performance and capacity independently to accommodate future growth without costly overprovisioning.
The architect is proposing HPE GreenLake for File Storage.
Which architectural features of this solution directly address the customer's requirements? (Select all that apply.)
```
Customer Requirements:
1. High performance for AI training workloads.
2. Independent scaling of performance and capacity.
```
Answer: A,D
NEW QUESTION # 54
An architect is designing an infrastructure solution for an AI workload that involves processing massive datasets for training. The goal is to minimize data transfer latency between the storage system and the GPUs in the compute nodes. The architect wants to enable the NVIDIA GPUs to fetch data directly from the NVMe storage array, bypassing the server's CPU and main memory.
Review the proposed architectural components:
```
- Compute Nodes: HPE ProLiant DL380a Gen11 with NVIDIA H100 GPUs
- Storage: HPE GreenLake for File Storage
- Interconnect: Ethernet with RoCE support
```
Which technology must be enabled and properly configured across these components to achieve this direct GPU-to-storage data path? (Choose 2.)
Answer: B,D
NEW QUESTION # 55
An organization is deploying a multi-tenant AI environment using HPE Private Cloud AI. They need to run several smaller, independent AI inference workloads on a single, powerful NVIDIA H100 GPU to maximize resource utilization. Each workload must be securely isolated with its own dedicated portion of the GPU's compute and memory resources.
What key NVIDIA technology, supported on the Hopper architecture, allows for this secure partitioning of a single physical GPU? (Select all that apply.)
Answer: A,C
NEW QUESTION # 56
......
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