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HP HPE2-B08 Exam Syllabus Topics:

HPE Private Cloud AI Solutions Sample Questions:

1. The three pre-defined sizes (Small, Medium, Large) of HPE Private Cloud AI are designed to address different primary workloads. Match the configuration size to its intended primary workload.
*Configuration Size:
1. Small
2. Medium
3. Large
*Primary Workload:
a. AI Inferencing with RAG and large-scale Fine-Tuning
b. AI Inferencing
c. AI Inferencing with RAG

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

2. An architect is designing a distributed training solution using HPE Private Cloud AI. The solution involves multiple HPE ProLiant DL380a servers, each with four NVIDIA H100 GPUs.
Review the logical network topology:
```
[HPE ProLiant Server 1] \
- GPU 0 <--> GPU 1 (Internal)
- GPU 2 <--> GPU 3 (Internal)
\
[NVIDIA Spectrum Switch (RoCE)] <--> [HPE GreenLake for File Storage]
/
[HPE ProLiant Server 2] /
- GPU 0 <--> GPU 1 (Internal)
- GPU 2 <--> GPU 3 (Internal)
```
Which interconnect technology facilitates the highest-bandwidth communication path labeled "(Internal)" for scaling the training job across the GPUs within a single server?

A) GPUDirect Storage (GDS)
B) PCI Express (PCIe) Gen5
C) RDMA over Converged Ethernet (RoCE)
D) NVIDIA NVLink Bridge

3. 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 provisions the underlying Kubernetes cluster and physical servers.
C) NIM provides a framework that packages the model into an optimized, ready-to-deploy container with a standard API.
D) NIM directly connects to the raw data sources to perform data preparation and cleaning.

4. 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 Alletra Storage MP with RoCE-capable network adapters
C) HPE ProLiant DL380a Gen11 server with GPUDirect Storage (GDS)
D) HPE ProLiant DL380a Gen12 server with an NVIDIA NVLink Bridge

5. 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 Alletra Storage MP with RoCE-capable network adapters
C) HPE ProLiant DL380a Gen11 server with GPUDirect Storage (GDS)
D) HPE ProLiant DL380a Gen12 server with an NVIDIA NVLink Bridge

Solutions: