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HPE Private Cloud AI Solutions Sample Questions:

1. A customer needs a solution for AI inferencing at the edge for a computer vision application. They have determined a Small configuration of HPE Private Cloud AI is sufficient. The solution also requires several value-added services, including an AI transformation workshop and a data design service.
How should an architect attach these services in One Config Advanced (OCA)?

A) The services must be quoted separately from the hardware and software configuration.
B) The services can be selected and added to the configuration as optional line items within OCA.
C) The architect must open a support ticket with HPE to have the services added to the quote.
D) The services are automatically included in the Smart Template and require no action.

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

3. 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 DL320 Gen11 server with NVIDIA L4 GPUs
D) HPE ProLiant DL384 Gen12 server with NVIDIA H200 NVL GPUs

4. When a developer deploys an AI model using an NVIDIA Inference Microservice (NIM) on HPE Private Cloud AI, the NIM automatically detects the underlying NVIDIA GPU hardware.
The developer sees the following status message upon deployment:
```
NIM Status:
- Model: mistral-7b-instruct-v0.2
- Status: Running
- Engine Profile: Selected 'TensorRT-LLM FP8' for optimal latency on detected Hopper-class GPU.
```
What does the selection of the 'TensorRT-LLM FP8' engine profile indicate?

A) The model is being automatically fine-tuned by the NIM using the TensorRT framework.
B) The NIM container has failed because it requires an FP32 profile to run.
C) The underlying GPU supports the Hopper architecture, and NIM has optimized the model using a lower precision format for faster performance.
D) The NIM container is running on a CPU-only node and is emulating a GPU.

5. A customer's ML Engineer states, "We need to deploy our trained models, and our top priority is simplifying the process. We want to treat our models like cattle, not pets-packaging them into standardized, optimized containers that we can deploy and scale easily via an API." This statement describes the primary benefit of which software component in the HPE Private Cloud AI stack?

A) NVIDIA NIM (NVIDIA Inference Microservices)
B) Apache Airflow
C) JupyterLab
D) HPE Data Fabric

Solutions: