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HP HPE2-B08 Exam Syllabus Topics:

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 services are automatically included in the Smart Template and require no action.
D) The architect must open a support ticket with HPE to have the services added to the quote.

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

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

4. A data analytics team is running workloads on an HPE Private Cloud AI solution. They observe that a data ingestion job is not meeting performance expectations, suspecting a CPU bottleneck. They believe the application is not correctly leveraging GPUDirect Storage (GDS), forcing data to be copied through the server's main memory before reaching the GPU.
Which are valid reasons why GDS might not be functioning correctly? (Choose 3.)

A) The HPE GreenLake for File Storage array is using SATA SSDs instead of NVMe SSDs.
B) The application is using a standard TCP/IP socket for data transfer instead of an RDMA-based library.
C) The NVIDIA GPUs have been configured with Multi-Instance GPU (MIG), which enhances GDS performance.
D) The network switches are not configured for lossless operation (e.g., PFC is disabled).
E) The NVIDIA peer memory driver has not been installed on the guest VM.

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

Solutions: