HP HPE2-B08 : HPE Private Cloud AI Solutions
- Exam Code: HPE2-B08
- Exam Name: HPE Private Cloud AI Solutions
- Updated: Sep 18, 2026
- Q & A: 87 Questions and Answers
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HP HPE2-B08 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Architecting HPE Private Cloud AI Solutions | 20% | - Describe the AI/ML lifecycle and data pipeline requirements - Explain common AI use cases and how they map to workloads - Explain the HPE Private Cloud AI sizing and configuration guidelines - Describe how HPE Private Cloud AI supports AI/ML workloads - Identify components of the HPE Private Cloud AI architecture |
| Managing and Operating HPE Private Cloud AI Solutions | 30% | - Identify how to manage storage and data resources - Explain how to monitor HPE Private Cloud AI performance and health - Describe how to manage users and access control - Identify troubleshooting procedures and common issues - Explain backup and recovery procedures - Describe the tools and methods for managing HPE Private Cloud AI |
| Supporting HPE Private Cloud AI Solutions | 20% | - Describe capacity planning and optimization best practices - Explain how to work with HPE support services - Describe support resources and documentation - Identify how to perform firmware and software updates |
| Installing and Configuring HPE Private Cloud AI Solutions | 30% | - Describe the prerequisites for installing HPE Private Cloud AI - Identify the steps to configure the HPE Private Cloud AI environment - Describe how to validate the HPE Private Cloud AI installation - Identify how to access and use HPE Private Cloud AI management interfaces - Explain how to deploy and configure HPE Private Cloud AI components |
HPE Private Cloud AI Solutions Sample Questions:
A company has developed a custom fraud detection model. Their data science team wants to deploy this model into production for other applications to use. They want to avoid the complexity of manually configuring the serving environment, optimizing for hardware, and creating a scalable API endpoint.
How does using an NVIDIA Inference Microservice (NIM) within HPE Private Cloud AI simplify this process?
- A. NIM provides a framework that packages the model into an optimized, ready-to-deploy container with a standard API.
- B. NIM automatically fine-tunes the model on new data to improve its accuracy.
- C. NIM directly connects to the raw data sources to perform data preparation and cleaning.
- D. NIM provisions the underlying Kubernetes cluster and physical servers.
Correct Answer: A 🗳️
An architect is in a discovery call with a customer who describes their project: "Our primary goal is to take our massive, proprietary dataset of chemical compound interactions and continuously update our foundational AI model's internal parameters to create a new, specialized model for drug discovery. This process runs 24/7 on a large GPU cluster." How should the architect classify this primary AI workload?
- A. AI Model Training / Fine-tuning
- B. Retrieval-Augmented Generation (RAG)
- C. Edge Computing
- D. AI Inferencing
Correct Answer: A 🗳️
A customer wants to build a configuration in One Config Advanced (OCA) for the HPE Private Cloud AI "Large - Standard" solution.
Which key components should the architect expect the Smart Template to include in the Bill of Materials (BOM)? (Choose 2.)
- A. HPE ProLiant DL380a Gen11 servers with NVIDIA H100 NVL GPUs.
- B. 4 worker nodes.
- C. HPE Cray compute nodes.
- D. HPE ProLiant DL380a Gen11 servers with NVIDIA L40S GPUs.
- E. 8 worker nodes.
Correct Answer: A,B 🗳️
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. Small - Standard (1-node)
- B. Medium - Expanded (4-node)
- C. Small - Expanded (2-node)
- D. Large - Standard (4-node)
Correct Answer: B 🗳️
An architect is explaining the structure of a simple Artificial Neural Network (ANN) to a client.
Review the following diagram of the network:
```
Input Layer Hidden Layer Output Layer
(Node) -- (Node) -- (Node)
(Node) / (Node) -\- (Node)
(Node) --/ (Node) / (Node)
```
Which component of the network is responsible for producing the final prediction, such as classifying an image as either a "cat" or a "dog"?
- A. The connections between the Input and Hidden layers
- B. Input Layer
- C. Hidden Layer
- D. Output Layer
Correct Answer: D 🗳️
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