HPE Private Cloud AI Solutions : HPE2-B08 valid dump

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HP HPE2-B08 Exam Syllabus Topics:

HPE Private Cloud AI Solutions Sample Questions:

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

2. A company has developed a custom fraud detection model. Their data science team wants to deploy this model into production for other applications to use. They want to avoid the complexity of manually configuring the serving environment, optimizing for hardware, and creating a scalable API endpoint.
How does using an NVIDIA Inference Microservice (NIM) within HPE Private Cloud AI simplify this process?

A) NIM automatically fine-tunes the model on new data to improve its accuracy.
B) NIM provides a framework that packages the model into an optimized, ready-to-deploy container with a standard API.
C) NIM provisions the underlying Kubernetes cluster and physical servers.
D) NIM directly connects to the raw data sources to perform data preparation and cleaning.

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

4. 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

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

Solutions: