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HPE Private Cloud AI Solutions : HPE2-B08
Exam Code: HPE2-B08
Exam Name: HPE Private Cloud AI Solutions
Updated: Sep 10, 2026
Q & A: 87 Questions and Answers
HPE2-B08 Free Demo download
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HP HPE2-B08 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Infrastructure Components of HPE Private Cloud AI with NVIDIA | 20% | - Infrastructure capabilities for AI workload requirements - Benefits of HPE and NVIDIA integrated infrastructure |
| Topic 2: Software Components of HPE Private Cloud AI with NVIDIA | 20% | - Software functions supporting AI operations - Benefits of HPE and NVIDIA software stack |
| Topic 3: Customer Assessment and Solution Positioning | 15% | - Assess AI maturity, workload characteristics and use cases - Position appropriate HPE AI solutions |
| Topic 4: Fundamental AI Concepts | 28% | - Impact of AI on industries and infrastructure requirements - General AI concepts, applications and workloads |
| Topic 5: Solution Sizing and Configuration | 17% | - Building configurations via One Config Advanced (OCA) - Using HPE Intelligent Configurator for sizing - Differences between configuration sizes and options |
HPE Private Cloud AI Solutions Sample Questions:
Question #1
A customer needs a solution for two primary workloads: large-scale model training and real-time inference. They have a team of data scientists who are constantly developing new models and a separate operations team that deploys and manages these models in production.
Which statement best describes how the different stakeholders would interact with the HPE Private Cloud AI solution?
- A. Both the data scientists and the operations team would use the HPE Intelligent Configurator to manage the models.
- B. The data scientists would use NVIDIA NIMs to train the models, and the operations team would use HPE Data Fabric to serve them.
- C. The data scientists would primarily use tools within HPE AI Essentials (like JupyterLab and MLFlow) for model development, while the operations team would use NVIDIA NIMs to deploy the finished models.
- D. The entire process for all stakeholders is managed through the server's iLO interface.
Answer: C
Question #2
You are positioning HPE Private Cloud AI to a customer who is an "AI Pro" and wants to scale their generative AI efforts.
Which key capabilities of the solution would you emphasize to this customer? (Select all that apply.)
- A. Its ability to run on a single, low-power server for maximum efficiency.
- B. Its turnkey nature, which simplifies deployment and allows their expert team to focus on building models rather than integrating infrastructure.
- C. The inclusion of a full-stack, enterprise-supported software suite including NVIDIA AI Enterprise and HPE AI Essentials.
- D. The option to choose a Large configuration with powerful NVIDIA H100 GPUs and a high-speed RoCE fabric for demanding fine-tuning workloads.
- E. A simple, web-based interface for beginners to learn about AI concepts.
Answer: B,C,D
Question #3
An architect is building a final configuration for an HPE Private Cloud AI solution using One Config Advanced (OCA). After selecting the "HPE Private Cloud AI - Medium Expanded" Smart Template, they review the generated Bill of Materials (BOM).
Which components are characteristic of the Medium Expanded configuration that the architect should expect to see in the OCA-generated BOM? (Select all that apply.)
- A. 16x NVIDIA L40S GPUs
- B. 2x NVIDIA SN4700M Switches
- C. 1x HPE ProLiant DL380a Gen11 Server
- D. 2x HPE Aruba Networking CX 6300M Switches
- E. 4x HPE ProLiant DL380a Gen11 Servers
- F. 16x NVIDIA H100 NVL GPUs
Answer: A,B,D,E
Question #4
An organization is deploying a multi-tenant AI environment using HPE Private Cloud AI. They need to run several smaller, independent AI inference workloads on a single, powerful NVIDIA H100 GPU to maximize resource utilization. Each workload must be securely isolated with its own dedicated portion of the GPU's compute and memory resources.
What key NVIDIA technology, supported on the Hopper architecture, allows for this secure partitioning of a single physical GPU? (Select all that apply.)
- A. Multi-Instance GPU (MIG)
- B. NVLink Bridge
- C. Remote Direct Memory Access (RDMA)
- D. GPUDirect Storage (GDS)
- E. Confidential Computing
Answer: A,E
Question #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. Fine-tune a pre-trained model daily by retraining it on the entire set of policy documents.
- B. Train a new LLM from scratch using only the company's policy documents to ensure data privacy.
- C. Use a pre-trained computer vision model to extract text from the policy documents.
- D. Use a pre-trained model and implement a Retrieval-Augmented Generation (RAG) framework.
Answer: D
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