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HP HPE2-B08 Exam Syllabus Topics:

SectionObjectives
Topic 1: AI Infrastructure Design- Compute and GPU considerations
- Networking for AI workloads
- Storage and data pipeline design
Topic 2: HPE Private Cloud AI Fundamentals- Core AI workload characteristics
- Overview of private cloud AI concepts
Topic 3: Deployment and Operations- Lifecycle management of AI infrastructure
- Deployment models for AI solutions
- Monitoring and optimization
Topic 4: HPE GreenLake for AI Solutions- Consumption-based IT model for AI
- GreenLake architecture and services
Topic 5: Data Management and Governance- Data governance and compliance
- Data lifecycle management
Topic 6: Security in Private Cloud AI- Workload and data protection
- Identity and access management

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HPE Private Cloud AI Solutions Sample Questions (Q68-Q73):

NEW QUESTION # 68
A customer is expanding their HPE Private Cloud AI "Medium" configuration to support a new generative AI inferencing workload. They are concerned about network congestion and latency, as the AI workload is known to generate large, sudden bursts of traffic between the compute nodes and the storage system.
The solution uses NVIDIA Spectrum SN4700M switches for the AI interconnect.
Which feature of these switches is specifically designed to handle bursty traffic and prevent packet loss in a lossless Ethernet fabric?

Answer: B


NEW QUESTION # 69
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?

Answer: C


NEW QUESTION # 70
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?

Answer: A


NEW QUESTION # 71
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.)

Answer: C,E


NEW QUESTION # 72
A customer needs to run a generative AI workload at multiple, dispersed edge locations. Each location has significant space and power constraints. The workload is inference-only and does not require the absolute highest performance, but rather a balance of good performance and energy efficiency.
Which HPE ProLiant server and NVIDIA GPU combination is specifically positioned for this type of edge AI use case?

Answer: C


NEW QUESTION # 73
......

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