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HP HPE2-B08 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Architecting HPE Private Cloud AI Solutions | 20% | - Explain the HPE Private Cloud AI sizing and configuration guidelines - Explain common AI use cases and how they map to workloads - Identify components of the HPE Private Cloud AI architecture - Describe the AI/ML lifecycle and data pipeline requirements - Describe how HPE Private Cloud AI supports AI/ML workloads |
| Managing and Operating HPE Private Cloud AI Solutions | 30% | - Identify troubleshooting procedures and common issues - Explain backup and recovery procedures - Describe the tools and methods for managing HPE Private Cloud AI - Describe how to manage users and access control - Identify how to manage storage and data resources - Explain how to monitor HPE Private Cloud AI performance and health |
| Installing and Configuring HPE Private Cloud AI Solutions | 30% | - Describe the prerequisites for installing HPE Private Cloud AI - Explain how to deploy and configure HPE Private Cloud AI components - Identify the steps to configure the HPE Private Cloud AI environment - Identify how to access and use HPE Private Cloud AI management interfaces - Describe how to validate the HPE Private Cloud AI installation |
| Supporting HPE Private Cloud AI Solutions | 20% | - Describe capacity planning and optimization best practices - Explain how to work with HPE support services - Identify how to perform firmware and software updates - Describe support resources and documentation |
HPE Private Cloud AI Solutions Sample Questions:
1. The three pre-defined sizes (Small, Medium, Large) of HPE Private Cloud AI are designed to address different primary workloads. Match the configuration size to its intended primary workload.
*Configuration Size:
1. Small
2. Medium
3. Large
*Primary Workload:
a. AI Inferencing with RAG and large-scale Fine-Tuning
b. AI Inferencing
c. AI Inferencing with RAG
A) 1-a, 2-b, 3-c
B) 1-b, 2-a, 3-c
C) 1-c, 2-a, 3-b
D) 1-b, 2-c, 3-a
2. An 'AI Pro' customer wants to deploy a solution to create 'digital twins' of their manufacturing equipment for predictive maintenance simulations. This workload requires significant GPU power for both the simulation and the underlying AI model.
Which HPE solution should be positioned as the primary platform for this advanced, data-center based workload?
A) The customer's existing VDI environment.
B) HPE Private Cloud AI with NVIDIA.
C) A standard data warehouse solution without GPU acceleration.
D) Individual HPE ProLiant DL320 Gen11 servers at the edge.
3. A customer wants to deploy a turnkey private cloud for a variety of generative AI workloads, including RAG-based chatbots and some model fine-tuning. One of their key IT stakeholders is the data engineer.
Which specific challenge for a data engineer is directly addressed by the HPE Data Fabric component within HPE Private Cloud AI?
A) The complexity of writing Python code in a Jupyter Notebook.
B) The difficulty of managing GPU cluster utilization for training jobs.
C) The effort required to create and manage data pipelines and unify diverse, siloed data sources (files, objects, tables) into a single accessible platform.
D) The high cost of NVIDIA AI Enterprise software licenses.
4. An architect has generated a configuration for HPE Private Cloud AI in OCA. The customer wants to know if they can use their existing data center network switches for the high-performance AI interconnect instead of the NVIDIA Spectrum switches included in the BOM.
What is the correct response?
A) Yes, as long as the existing switches are also from NVIDIA, they are compatible.
B) No, the NVIDIA Spectrum switches are part of the pre-validated, turnkey solution and are required for performance and support.
C) Yes, any 200GbE switch can be substituted to lower the cost.
D) No, because the HPE Private Cloud AI solution is air-gapped and cannot connect to external switches.
5. 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?
A) HPE ProLiant DL384 Gen12 server with NVIDIA H200 NVL GPUs
B) HPE ProLiant DL380a server with NVIDIA H100 NVL GPUs
C) HPE Cray system with NVIDIA Grace Hopper Superchips
D) HPE ProLiant DL320 Gen11 server with NVIDIA L4 GPUs
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: B | Question # 3 Answer: C | Question # 4 Answer: B | Question # 5 Answer: D |



