Study with HPE2-B08 most valid questions & verified answers

An architect is designing a high-performance computing environment for a customer with two distinct, demanding workloads:
1. A large-scale, multi-node deep learning model that requires the fastest possible server-to-server communication.
2. A data-intensive analytics workload that requires the fastest possible data ingestion from an HPE GreenLake for File Storage array.
Which combination of technologies from the HPE Private Cloud AI solution should the architect select to optimize both workloads? (Select all that apply.)

• A. GPUDirect Storage (GDS) to enable a direct data path between storage and GPUs for the analytics workload.
• B. InfiniBand for the server-to-server fabric and Fibre Channel for the storage fabric.
• C. NVLink for intra-server GPU communication and RoCE for the server-to-server and server-to-storage fabric.
• D. Multi-Instance GPU (MIG) to partition GPUs for each workload.
• E. A flat TCP/IP network for simplicity, relying on the speed of the 200GbE adapters.

An architect is evaluating the high operational costs associated with their company's internal AI platform. The primary workload involves fine-tuning a 70-billion parameter LLM for various departmental tasks. The team reports that the GPU cluster utilization is low, and jobs often fail, requiring manual restarts.
They are reviewing the platform's configuration:
```
- Model: Llama 2 70B
- Task: Supervised Fine-Tuning
- Cluster Size: 16x nodes, each with 4x NVIDIA A100 GPUs
- Scheduling: Manual job submission via SSH scripts
- Data Management: Datasets manually copied to local storage on each node
- Collaboration: Code and models shared via a central Git repository
```
Which NVIDIA AI Enterprise component is specifically designed to address the challenges of low GPU utilization and manual job management in a multi-node training environment like this?

• A. NVIDIA NIM (NVIDIA Inference Microservices)
• B. NVIDIA RAPIDS
• C. HPE Machine Learning Development Environment
• D. NVIDIA Triton Inference Server

An 'AI Pro' customer wants to deploy a solution to create 'digital twins' of their manufacturing equipment for predictive maintenance simulations. This workload requires significant GPU power for both the simulation and the underlying AI model.
Which HPE solution should be positioned as the primary platform for this advanced, data-center based workload?

• A. Individual HPE ProLiant DL320 Gen11 servers at the edge.
• B. A standard data warehouse solution without GPU acceleration.
• C. The customer's existing VDI environment.
• D. HPE Private Cloud AI with NVIDIA.

An architect is explaining the HPE Private Cloud AI configurations to a customer.
What is the key differentiator between the "Large" configuration and the "Small" and "Medium" configurations in terms of hardware?

• A. The Large configuration is the only one that includes HPE GreenLake for File Storage.
• B. The Large configuration uses NVIDIA H100 NVL GPUs, while the Small and Medium configurations use NVIDIA L40S GPUs.
• C. The Large configuration is deployed in a single rack, while the Small and Medium require two racks.
• D. The Large configuration uses AMD processors, while the Small and Medium configurations use Intel processors.

An architect is sizing an HPE Private Cloud AI solution. The customer plans to deploy a generative AI application for 150 concurrent users that requires Retrieval-Augmented Generation (RAG).
The architect enters the following into the HPE Intelligent Configurator:
```
- Use case: Text Generation
- Number of users: 100-250
- RAG: Yes
- Model: Llama 2 13B (tool default for this use case)
```
Based on the provided inputs, which HPE Private Cloud AI configuration will the HPE Intelligent Configurator most likely recommend?

• A. Large - Standard (4-node)
• B. Medium - Expanded (4-node)
• C. Small - Expanded (2-node)
• D. Small - Standard (1-node)