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HP HPE2-B08 Exam Syllabus Topics:

HPE Private Cloud AI Solutions Sample Questions:

1. An architect has used the HPE Intelligent Configurator and determined that a "Medium - Standard" configuration is required.
When creating the final quote in One Config Advanced (OCA), what is the mandatory prerequisite that the customer is responsible for providing for the solution to be valid?

A) A list of all end-users who will access the system.
B) A subscription to the NVIDIA NGC catalog for downloading AI models.
C) A valid, bring-your-own-license (BYOL) for VMware vSphere Foundation (VVF) or VMware Cloud Foundation (VCF).
D) A rack elevation diagram for the data center.

2. A customer is planning to deploy an HPE Private Cloud AI solution. Their IT department has provided a worksheet detailing the workload requirements. The solution architect needs to select the appropriate configuration size.
Review the customer's requirements:
```
- Primary Workload: Fine-tuning a 70-billion parameter foundation model
- Secondary Workload: AI Inferencing with RAG
- User Base: 50 developers during tuning; 300 concurrent users for inference
- Data Sensitivity: High (must remain on-premises)
- Power/Cooling: Standard data center air cooling available
```
Which HPE Private Cloud AI configuration is required to meet these demands, and what is the key component that justifies this choice? (Choose 2.)

A) The solution requires a Large configuration.
B) The key component is the HPE ProLiant DL325 server in the management plane.
C) The key component is the NVIDIA L40S GPU, which is optimized for RAG workloads.
D) The key component is the NVIDIA H100 NVL GPU, designed for large-scale training and fine-tuning.
E) The solution requires a Medium configuration.

3. An architect is sizing an HPE Private Cloud AI solution. The customer plans to deploy a generative AI application for 150 concurrent users that requires Retrieval-Augmented Generation (RAG).
The architect enters the following into the HPE Intelligent Configurator:
```
- Use case: Text Generation
- Number of users: 100-250
- RAG: Yes
- Model: Llama 2 13B (tool default for this use case)
```
Based on the provided inputs, which HPE Private Cloud AI configuration will the HPE Intelligent Configurator most likely recommend?

A) Medium - Expanded (4-node)
B) Small - Standard (1-node)
C) Large - Standard (4-node)
D) Small - Expanded (2-node)

4. A customer needs a solution for their deployed customer service chatbot. They state: "We don't need to change the model itself, but we need the chatbot to answer questions using our product documentation, which is updated every night. The answers must be fast and based on the latest documents." How would you categorize this workload?

A) A RAG (Retrieval-Augmented Generation) inferencing workload.
B) A classic AI inferencing workload.
C) A model development and experimentation workload.
D) A large-scale model training workload.

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 relies on traditional hard disk drives (HDDs) for cost-effective capacity, with a small flash tier for caching.
B) It includes HPE Data Fabric, providing a global namespace across the worker nodes' local drives.
C) It uses a disaggregated architecture, allowing controller nodes (performance) and storage shelves (capacity) to be scaled separately.
D) It is managed exclusively through a command-line interface (CLI) for maximum control.
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: