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HP HPE2-B08 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Software Components of HPE Private Cloud AI with NVIDIA | 20% | - Software functions supporting AI operations - Benefits of HPE and NVIDIA software stack |
| Fundamental AI Concepts | 28% | - General AI concepts, applications and workloads - Impact of AI on industries and infrastructure requirements |
| Solution Sizing and Configuration | 17% | - Differences between configuration sizes and options - Building configurations via One Config Advanced (OCA) - Using HPE Intelligent Configurator for sizing |
| Customer Assessment and Solution Positioning | 15% | - Assess AI maturity, workload characteristics and use cases - Position appropriate HPE AI solutions |
| Infrastructure Components of HPE Private Cloud AI with NVIDIA | 20% | - Benefits of HPE and NVIDIA integrated infrastructure - Infrastructure capabilities for AI workload requirements |
HPE Private Cloud AI Solutions Sample Questions:
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)
Correct Answer: A 🗳️
An architect is evaluating the high operational costs associated with their company's internal AI platform. The primary workload involves fine-tuning a 70-billion parameter LLM for various departmental tasks. The team reports that the GPU cluster utilization is low, and jobs often fail, requiring manual restarts.
They are reviewing the platform's configuration:
```
- Model: Llama 2 70B
- Task: Supervised Fine-Tuning
- Cluster Size: 16x nodes, each with 4x NVIDIA A100 GPUs
- Scheduling: Manual job submission via SSH scripts
- Data Management: Datasets manually copied to local storage on each node
- Collaboration: Code and models shared via a central Git repository
```
Which NVIDIA AI Enterprise component is specifically designed to address the challenges of low GPU utilization and manual job management in a multi-node training environment like this?
- A. NVIDIA RAPIDS
- B. HPE Machine Learning Development Environment
- C. NVIDIA NIM (NVIDIA Inference Microservices)
- D. NVIDIA Triton Inference Server
Correct Answer: B 🗳️
After an architect selects the "HPE Private Cloud AI - Large - Expanded" Smart Template in OCA, they see it includes a significant number of "HPE Factory Express Complex Unit of SVC" services.
What is the purpose of these bundled services?
- A. To provide a credit for future software purchases on the HPE GreenLake cloud.
- B. To provide on-site training for the customer's data science team.
- C. To cover the pre-integration and configuration of the entire solution at an HPE factory before shipment.
- D. To allow the customer to exchange the hardware for the next generation at no cost.
Correct Answer: C 🗳️
An architect uses the HPE Intelligent Configurator for a customer with 120 concurrent users for a text generation task with RAG. The tool recommends a "Medium - Expanded (4-node)" configuration. The customer then reveals they want to use a smaller, more efficient 7B parameter model instead of the 13B model the tool defaulted to.
How will this change in model size likely affect the sizing tool's recommendation?
- A. It will require the architect to switch to the "application capacity" sizing method.
- B. It will downgrade the recommendation to a "Medium - Standard (2-node)" configuration.
- C. It will have no effect, as the number of users is the primary factor.
- D. It will upgrade the recommendation to a "Large - Standard (4-node)" configuration.
Correct Answer: B 🗳️
An architect is designing an infrastructure solution for an AI workload that involves processing massive datasets for training. The goal is to minimize data transfer latency between the storage system and the GPUs in the compute nodes. The architect wants to enable the NVIDIA GPUs to fetch data directly from the NVMe storage array, bypassing the server's CPU and main memory.
Review the proposed architectural components:
```
- Compute Nodes: HPE ProLiant DL380a Gen11 with NVIDIA H100 GPUs
- Storage: HPE GreenLake for File Storage
- Interconnect: Ethernet with RoCE support
```
Which technology must be enabled and properly configured across these components to achieve this direct GPU-to-storage data path? (Choose 2.)
- A. RDMA over Converged Ethernet (RoCE)
- B. NVLink
- C. Multi-Instance GPU (MIG)
- D. GPUDirect Storage (GDS)
- E. Confidential Computing
Correct Answer: A,D 🗳️
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Viewing questions 1-5 out of 87 questions.
