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| Section | Objectives |
|---|---|
| AI Infrastructure Design | - Storage and data pipeline design - Networking for AI workloads - Compute and GPU considerations |
| HPE Private Cloud AI Fundamentals | - Overview of private cloud AI concepts - Core AI workload characteristics |
| Data Management and Governance | - Data governance and compliance - Data lifecycle management |
| Deployment and Operations | - Monitoring and optimization - Lifecycle management of AI infrastructure - Deployment models for AI solutions |
| Security in Private Cloud AI | - Workload and data protection - Identity and access management |
| HPE GreenLake for AI Solutions | - GreenLake architecture and services - Consumption-based IT model for AI |
1. An architect is building a final configuration for an HPE Private Cloud AI solution using One Config Advanced (OCA). After selecting the "HPE Private Cloud AI - Medium Expanded" Smart Template, they review the generated Bill of Materials (BOM).
Which components are characteristic of the Medium Expanded configuration that the architect should expect to see in the OCA-generated BOM? (Select all that apply.)
A) 2x HPE Aruba Networking CX 6300M Switches
B) 16x NVIDIA L40S GPUs
C) 2x NVIDIA SN4700M Switches
D) 1x HPE ProLiant DL380a Gen11 Server
E) 16x NVIDIA H100 NVL GPUs
F) 4x HPE ProLiant DL380a Gen11 Servers
2. What is the primary architectural advantage of the NVIDIA Grace Hopper Superchip (e.g., GH200) for large-scale AI workloads?
A) It replaces the need for server memory (DRAM) by using the GPU's global memory exclusively.
B) It uses on-chip encryption to create a confidential computing environment for the CPU.
C) It combines a CPU and a GPU on a single superchip, connected by a high-speed, low-latency NVLink-C2C interconnect.
D) It is the first NVIDIA GPU to feature fourth-generation Tensor Cores for enhanced matrix calculations.
3. A large enterprise is adopting HPE Private Cloud AI and needs to accelerate the development of several generative AI applications. Their data science team wants to leverage pre-trained foundation models from NVIDIA but needs to customize them for specific business tasks like contract summarization and internal policy Q&A.
Which NVIDIA AI Enterprise software framework provides a comprehensive, end-to-end toolkit for curating data, customizing models using techniques like PEFT, and implementing guardrails for safe deployment?
```
Customer Goal:
- Accelerate development of custom generative AI apps
- Utilize pre-trained foundation models
- Require tools for data prep, model customization, and safety
```
A) NVIDIA RAPIDS
B) NVIDIA NIM (NVIDIA Inference Microservices)
C) NVIDIA NeMo
D) NVIDIA Triton Inference Server
4. A customer is expanding their HPE Private Cloud AI "Medium" configuration to support a new generative AI inferencing workload. They are concerned about network congestion and latency, as the AI workload is known to generate large, sudden bursts of traffic between the compute nodes and the storage system.
The solution uses NVIDIA Spectrum SN4700M switches for the AI interconnect.
Which feature of these switches is specifically designed to handle bursty traffic and prevent packet loss in a lossless Ethernet fabric?
A) Integrated Silicon Root of Trust to validate the switch firmware integrity.
B) Support for Fibre Channel over Ethernet (FCoE) encapsulation.
C) The ability to route traffic based on application-layer metadata.
D) A fully shared buffer architecture that can dynamically absorb traffic bursts from any port.
5. 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.)
A) GPUDirect Storage (GDS)
B) NVLink
C) RDMA over Converged Ethernet (RoCE)
D) Confidential Computing
E) Multi-Instance GPU (MIG)
Solutions:
| Question # 1 Answer: A,B,C,F | Question # 2 Answer: C | Question # 3 Answer: C | Question # 4 Answer: D | Question # 5 Answer: A,C |
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