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HP HPE2-B08 Exam Syllabus Topics:

HPE Private Cloud AI Solutions Sample Questions:

1. An architect is positioning an HPE Private Cloud AI solution to a customer who is an "AI Pro." The customer's CIO is the key decision maker.
Which benefits of the solution would be most compelling to this stakeholder? (Choose 2.)

A) A predictable, cloud-like operational expense model and a clear return on investment (ROI) by accelerating time-to-value for AI projects.
B) Access to a command-line interface for the network switches.
C) The ability to write Python code in a Jupyter Notebook.
D) The specific version of the CUDA driver used by the NVIDIA GPUs.
E) The ability to use NVIDIA NIMs to drastically simplify and standardize model deployment, reducing operational complexity and cost.

2. A customer's distributed AI training job is experiencing poor performance. An analysis reveals high network latency. The architect suspects a misconfiguration in the NVIDIA Spectrum switches that form the RoCE-based AI fabric.
They review the following configuration snippet from one of the switch ports connected to a compute node:
```
interface ethernet 1/1
description "Connection to PCAI-Node-1"
speed 200G
mtu 9216
switchport mode trunk
## Priority Flow Control (PFC) is disabled on this port
```
What is the most likely cause of the performance issue?

A) Priority Flow Control (PFC) is disabled, which is mandatory for a lossless RoCE fabric.
B) The port speed is not sufficient for the connected H100 GPU.
C) The MTU size is too small for AI workloads.
D) The port is incorrectly configured in trunk mode instead of access mode.

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

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

5. An architect is designing an AI solution for a financial services company that needs to build a chatbot.
The chatbot must answer customer queries using the company's latest internal policy documents, which are updated daily. The company has a limited budget and no data scientists available for a lengthy model retraining project.
Which approach should the architect recommend?

A) Use a pre-trained model and implement a Retrieval-Augmented Generation (RAG) framework.
B) Fine-tune a pre-trained model daily by retraining it on the entire set of policy documents.
C) Use a pre-trained computer vision model to extract text from the policy documents.
D) Train a new LLM from scratch using only the company's policy documents to ensure data privacy.

Solutions: