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HP HPE2-B08 Exam Syllabus Topics:

HPE Private Cloud AI Solutions Sample Questions:

1. An AI development team is training a large language model that exceeds the memory capacity of a single GPU. To continue training, they need to distribute the workload across multiple GPUs within the same server.
Which combination of HPE server and NVIDIA technology is specifically designed to provide a high- bandwidth, direct communication path between GPUs, bypassing the PCIe bus for improved performance in multi-GPU training?

A) HPE ProLiant DL325 Gen11 server with NVIDIA L4 GPUs
B) HPE ProLiant DL380a Gen11 server with GPUDirect Storage (GDS)
C) HPE ProLiant DL380a Gen12 server with an NVIDIA NVLink Bridge
D) HPE Alletra Storage MP with RoCE-capable network adapters

2. A data science team is struggling to manage their AI/ML projects. They use a variety of open-source tools for data preparation, training, and MLOps, but integrating them is complex and time-consuming.
They need a unified platform that provides self-service access to a curated and pre-integrated set of these tools.
Which HPE Private Cloud AI software component is specifically designed to solve this problem?

A) HPE Intelligent Configurator
B) HPE AI Essentials
C) NVIDIA NIM
D) HPE GreenLake for File Storage

3. An organization is using HPE Private Cloud AI to manage a large, multi-tenant Kubernetes environment for its data science teams. They need to ensure that data access is securely managed and that each team's data is isolated within a logical, policy-driven boundary. Additionally, they need to store and access data from multiple sources, including NFS clients and S3-compatible object stores.
Which software components of the HPE Private Cloud AI stack work together to provide this unified, policy-driven, multi-protocol data access? (Select all that apply.)

A) Kyverno
B) NVIDIA NIM (NVIDIA Inference Microservices)
C) HPE Data Fabric
D) HPE AI Essentials
E) HPE GreenLake for File Storage

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

5. An architect has generated a configuration for HPE Private Cloud AI in OCA. The customer wants to know if they can use their existing data center network switches for the high-performance AI interconnect instead of the NVIDIA Spectrum switches included in the BOM.
What is the correct response?

A) No, because the HPE Private Cloud AI solution is air-gapped and cannot connect to external switches.
B) Yes, as long as the existing switches are also from NVIDIA, they are compatible.
C) Yes, any 200GbE switch can be substituted to lower the cost.
D) No, the NVIDIA Spectrum switches are part of the pre-validated, turnkey solution and are required for performance and support.

Solutions: