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

2. An enterprise architecture team is debating the best method to adapt a general-purpose Large Language Model (LLM) for two different, highly-specialized internal use cases:
1. Use Case A: A customer support chatbot that must provide answers strictly based on a rapidly changing knowledge base of product manuals and technical notes. Verifiability and traceability of the information source are critical.
2. Use Case B: An internal code generation assistant that needs to learn the company's specific coding style, proprietary frameworks, and API usage patterns from a large, static codebase.
Which are the most appropriate strategies for these use cases? (Choose 2.)

A) Use RAG for Use Case B to allow the model to retrieve code snippets from the static codebase.
B) Use fine-tuning for Use Case B to embed the company-specific coding patterns and styles into the model's behavior.
C) Use Retrieval-Augmented Generation (RAG) for Use Case A to provide up-to-date, verifiable information at inference time.
D) Use fine-tuning for Use Case A to ensure the model deeply learns the product manual content.
E) Use both RAG and fine-tuning for both use cases as they are always used together.

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

4. A customer in the media and entertainment industry wants a solution to accelerate the creation of 3D animations and renderings. Their artists need to be able to generate complex visual AI content quickly.
The company has a mature AI practice and requires a powerful, data center-based solution.
Which HPE ProLiant server and NVIDIA GPU combination is purpose-built for these generative visual AI workloads?

A) HPE ProLiant DL320 Gen11 server with NVIDIA L4 GPUs
B) HPE ProLiant DL380a Gen11 server with NVIDIA L40S GPUs
C) HPE ProLiant DL380a Gen12 server with an NVIDIA NVLink Bridge but no GPUs
D) HPE ProLiant DL380a Gen11 server with CPU-only configuration

5. A customer is building a large-scale AI training cluster using HPE Private Cloud AI.
They have two main requirements for the storage solution:
1. It must provide extremely high throughput and low latency to feed data to a large cluster of NVIDIA H100 GPUs.
2. It must be able to scale performance and capacity independently to accommodate future growth without costly overprovisioning.
The architect is proposing HPE GreenLake for File Storage.
Which architectural features of this solution directly address the customer's requirements? (Select all that apply.)
```
Customer Requirements:
1. High performance for AI training workloads.
2. Independent scaling of performance and capacity.
```

A) It uses a disaggregated architecture, allowing controller nodes (performance) and storage shelves (capacity) to be scaled separately.
B) It includes HPE Data Fabric, providing a global namespace across the worker nodes' local drives.
C) It is managed exclusively through a command-line interface (CLI) for maximum control.
D) It relies on traditional hard disk drives (HDDs) for cost-effective capacity, with a small flash tier for caching.
E) It supports GPUDirect Storage (GDS) over a high-speed NVMe-oF (RoCE) network, enabling a direct, low-latency path between storage and GPUs.

Solutions: