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HPE Private Cloud AI Solutions Sample Questions:

1. An architect is positioning an HPE Private Cloud AI solution to a customer who is an "AI Pro." The customer's CIO is the key decision maker.
Which benefits of the solution would be most compelling to this stakeholder? (Choose 2.)

A) A predictable, cloud-like operational expense model and a clear return on investment (ROI) by accelerating time-to-value for AI projects.
B) The ability to use NVIDIA NIMs to drastically simplify and standardize model deployment, reducing operational complexity and cost.
C) The specific version of the CUDA driver used by the NVIDIA GPUs.
D) Access to a command-line interface for the network switches.
E) The ability to write Python code in a Jupyter Notebook.

2. A customer needs a solution for two primary workloads: large-scale model training and real-time inference. They have a team of data scientists who are constantly developing new models and a separate operations team that deploys and manages these models in production.
Which statement best describes how the different stakeholders would interact with the HPE Private Cloud AI solution?

A) The data scientists would primarily use tools within HPE AI Essentials (like JupyterLab and MLFlow) for model development, while the operations team would use NVIDIA NIMs to deploy the finished models.
B) Both the data scientists and the operations team would use the HPE Intelligent Configurator to manage the models.
C) The entire process for all stakeholders is managed through the server's iLO interface.
D) The data scientists would use NVIDIA NIMs to train the models, and the operations team would use HPE Data Fabric to serve them.

3. An architect is designing an AI solution for a financial services company that needs to build a chatbot.
The chatbot must answer customer queries using the company's latest internal policy documents, which are updated daily. The company has a limited budget and no data scientists available for a lengthy model retraining project.
Which approach should the architect recommend?

A) Use a pre-trained model and implement a Retrieval-Augmented Generation (RAG) framework.
B) Fine-tune a pre-trained model daily by retraining it on the entire set of policy documents.
C) Use a pre-trained computer vision model to extract text from the policy documents.
D) Train a new LLM from scratch using only the company's policy documents to ensure data privacy.

4. An organization is deploying a multi-tenant AI environment using HPE Private Cloud AI. They need to run several smaller, independent AI inference workloads on a single, powerful NVIDIA H100 GPU to maximize resource utilization. Each workload must be securely isolated with its own dedicated portion of the GPU's compute and memory resources.
What key NVIDIA technology, supported on the Hopper architecture, allows for this secure partitioning of a single physical GPU? (Select all that apply.)

A) GPUDirect Storage (GDS)
B) Confidential Computing
C) Remote Direct Memory Access (RDMA)
D) NVLink Bridge
E) Multi-Instance GPU (MIG)

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

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