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HPE Private Cloud AI Solutions Sample Questions:

1. A customer has selected the "HPE Private Cloud AI - Medium - Expanded" configuration.
Which of the following resource counts correctly describes this specific configuration?

A) 8 worker nodes, 32 total H100 NVL GPUs, dual rack
B) 2 worker nodes, 8 total L40S GPUs, single rack
C) 4 worker nodes, 16 total H100 NVL GPUs, dual rack
D) 4 worker nodes, 16 total L40S GPUs, single rack

2. A customer needs to run a generative AI workload at multiple, dispersed edge locations. Each location has significant space and power constraints. The workload is inference-only and does not require the absolute highest performance, but rather a balance of good performance and energy efficiency.
Which HPE ProLiant server and NVIDIA GPU combination is specifically positioned for this type of edge AI use case?

A) HPE Cray system with NVIDIA Grace Hopper Superchips
B) HPE ProLiant DL380a server with NVIDIA H100 NVL GPUs
C) HPE ProLiant DL384 Gen12 server with NVIDIA H200 NVL GPUs
D) HPE ProLiant DL320 Gen11 server with NVIDIA L4 GPUs

3. 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) The ability to route traffic based on application-layer metadata.
B) Support for Fibre Channel over Ethernet (FCoE) encapsulation.
C) Integrated Silicon Root of Trust to validate the switch firmware integrity.
D) A fully shared buffer architecture that can dynamically absorb traffic bursts from any port.

4. An architect is assessing a customer's workloads to position the correct HPE Private Cloud AI configuration. The customer has a single, massive task: training a foundational vision model from scratch using a petabyte-scale image dataset. The project is expected to take months and requires the absolute maximum compute performance available.
Which AI workload type does this represent, and which HPE solution is most appropriate?

A) Workload: AI Model Training; Solution: HPE Cray Systems
B) Workload: AI Inferencing; Solution: AI-optimized HPE ProLiant DL servers
C) Workload: Edge Computing; Solution: HPE ProLiant DL320 server
D) Workload: RAG; Solution: HPE Private Cloud AI - Medium configuration

5. An architect is designing an AI solution to create a medical chatbot that assists doctors by answering questions based on the latest published medical research. The system must be highly reliable, and its answers must be traceable to the source publications. The customer has highlighted that their internal data science team lacks the expertise for complex model retraining but can manage data ingestion pipelines.
Given the customer requirements, which architectural components should the architect include in the solution design? (Select all that apply.)
```
Customer Requirements:
- AI Use Case: Medical Research Q&A Chatbot
- Key Constraint: Information must be current and verifiable.
- Team Skills: Limited AI model training expertise.
- Data Source: Continuously updated database of medical journals.
```

A) A data ingestion and embedding pipeline to process and add new research to the vector database.
B) A vector database to store embeddings of the medical research papers.
C) A Retrieval-Augmented Generation (RAG) framework to query the vector database.
D) A pre-trained, general-purpose Large Language Model (LLM).
E) An edge-optimized server for model deployment in individual hospital rooms.
F) A process for daily fine-tuning of the base LLM on all new research papers.

Solutions: