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HP HPE2-B08 Exam Syllabus Topics:

HPE Private Cloud AI Solutions Sample Questions:

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

2. An architect is meeting with a prospective customer to determine the right HPE AI solution. The customer provides the following information about their situation.
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- AI Status: No formal AI strategy. One successful PoC for theft prevention using computer vision is running at a single store.
- Goal: Wants to explore creating a "digital twin" of their supply chain for simulation, but has no clear KPIs.
- Team: Two data scientists, one ML engineer.
- Infrastructure: Ad-hoc use of public cloud for the PoC; no standardized tech stack.
```
Based on this profile, which AI maturity level and corresponding HPE solution should the architect initially position? (Choose 2.)

A) Lead with HPE Private Cloud AI with NVIDIA.
B) The customer is an 'AI Pro'.
C) Lead with HPE Cray systems for the digital twin simulation.
D) The customer is an 'Early AI user'.
E) The customer is a 'Deployer of AI at scale'.

3. A customer has selected the "HPE Private Cloud AI - Medium - Expanded" configuration.
Which of the following resource counts correctly describes this specific configuration?

A) 4 worker nodes, 16 total L40S GPUs, single rack
B) 2 worker nodes, 8 total L40S GPUs, single rack
C) 8 worker nodes, 32 total H100 NVL GPUs, dual rack
D) 4 worker nodes, 16 total H100 NVL GPUs, dual rack

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

5. An architect is using the HPE One Config Advanced (OCA) Smart Template for "HPE Private Cloud AI
- Small - Standard".
What is the worker node configuration they should expect to see in the generated Bill of Materials (BOM)?

A) 1x HPE ProLiant DL380a Gen11 server with 4x NVIDIA L40S GPUs
B) 3x HPE ProLiant DL325 Gen11 servers for management
C) 4x HPE ProLiant DL380a Gen11 servers with a total of 16x NVIDIA H100 NVL GPUs
D) 2x HPE ProLiant DL380a Gen11 servers with a total of 8x NVIDIA L40S GPUs

Solutions: