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HPE Private Cloud AI Solutions Sample Questions:

1. An architect is selecting the appropriate HPE Private Cloud AI configuration for a customer. The customer's primary workload is fine-tuning a 70B parameter model, a task that is extremely sensitive to compute latency and requires the highest possible performance for multi-node distributed training.
Which specific infrastructure components included in the "Large" configuration of HPE Private Cloud AI are essential for meeting this high-performance training requirement? (Select all that apply.)

A) NVIDIA SN4700M switches supporting 200GbE RoCE
B) NVIDIA NVLink Bridge
C) HPE Aruba Networking CX 6300M switches
D) NVIDIA L40S GPUs
E) NVIDIA H100 NVL GPUs

2. 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) Small - Standard (1-node)
C) Small - Expanded (2-node)
D) Medium - Expanded (4-node)

3. 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) RDMA over Converged Ethernet (RoCE)
B) GPUDirect Storage (GDS)
C) Multi-Instance GPU (MIG)
D) Confidential Computing
E) NVLink

4. A large financial institution, a known "Deployer of AI at scale," needs to train a next-generation fraud detection model. This new model has over a trillion parameters, significantly larger than their current models, and requires an exascale-class computing solution to be trained in a reasonable timeframe.
Which HPE AI solution should be positioned to meet this customer's demanding requirement?

A) AI-optimized HPE ProLiant DL servers
B) HPE Private Cloud AI with NVIDIA, Large configuration
C) HPE Cray systems
D) HPE GreenLake for File Storage

5. Match the stakeholder in an AI project to their most likely primary concern.
Stakeholders:
1. Data Scientist
2. AI Decision Maker (CIO)
3. Data Engineer
Concerns:
a. "How can I easily build, manage, and automate data pipelines to feed our models?" b. "What is the total cost of ownership (TCO) and return on investment (ROI) for this AI initiative?" c. "Which hyperparameter tuning method will give my model the highest accuracy?"

A) 1-a, 2-c, 3-b
B) 1-b, 2-a, 3-c
C) 1-c, 2-b, 3-a
D) 1-c, 2-a, 3-b

Solutions: