HP HPE2-B08 : HPE Private Cloud AI Solutions

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HP HPE2-B08 Exam Syllabus Topics:

HPE Private Cloud AI Solutions Sample Questions:

Question #1
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. Small - Expanded (2-node)
B. Large - Standard (4-node)
C. Medium - Expanded (4-node)
D. Small - Standard (1-node)

Question #2
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: RAG; Solution: HPE Private Cloud AI - Medium configuration
B. Workload: Edge Computing; Solution: HPE ProLiant DL320 server
C. Workload: AI Model Training; Solution: HPE Cray Systems
D. Workload: AI Inferencing; Solution: AI-optimized HPE ProLiant DL servers

Question #3
A customer is considering the HPE Private Cloud AI solution. They need to run a moderately sized RAG application for 150 users. They do not have any fine-tuning requirements.
Why would an architect recommend a "Medium" configuration over a "Large" configuration for this customer?

A. The Medium configuration is the only one that includes the HPE AI Essentials software.
B. The Large configuration does not support Retrieval-Augmented Generation (RAG).
C. The Medium configuration has more storage capacity than the Large configuration.
D. The L40S GPUs in the Medium configuration are more cost-effective and power-efficient for this specific inference-heavy workload.

Question #4
An architect is designing a high-performance computing environment for a customer with two distinct, demanding workloads:
1. A large-scale, multi-node deep learning model that requires the fastest possible server-to-server communication.
2. A data-intensive analytics workload that requires the fastest possible data ingestion from an HPE GreenLake for File Storage array.
Which combination of technologies from the HPE Private Cloud AI solution should the architect select to optimize both workloads? (Select all that apply.)

A. Multi-Instance GPU (MIG) to partition GPUs for each workload.
B. A flat TCP/IP network for simplicity, relying on the speed of the 200GbE adapters.
C. InfiniBand for the server-to-server fabric and Fibre Channel for the storage fabric.
D. GPUDirect Storage (GDS) to enable a direct data path between storage and GPUs for the analytics workload.
E. NVLink for intra-server GPU communication and RoCE for the server-to-server and server-to-storage fabric.

Question #5
A development team reports that their custom-trained Large Language Model (LLM) is "hallucinating"
- generating factually incorrect or nonsensical information, especially when asked questions outside the scope of its training data. The model was created by fine-tuning a foundation model on a large but static internal dataset. The team wants to improve the model's factual accuracy and reliability without embarking on a new, large-scale training project.
Which are the most appropriate strategies to mitigate this issue? (Choose 2.)

A. Apply stricter content moderation and safety guardrails to the model's output.
B. Retrain the model from scratch using a much larger and more diverse public dataset.
C. Implement a Retrieval-Augmented Generation (RAG) framework to provide the model with verifiable, external context at inference time.
D. Increase the number of hidden layers in the model's architecture.
E. Reduce the "temperature" setting during inference to make the model's output less random and more focused.

Solutions: