HP HPE2-B08 : HPE Private Cloud AI Solutions
- Exam Code: HPE2-B08
- Exam Name: HPE Private Cloud AI Solutions
- Updated: Sep 16, 2026
- Q & A: 87 Questions and Answers
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HP HPE2-B08 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Infrastructure Components of HPE Private Cloud AI with NVIDIA | 20% | - Infrastructure capabilities for AI workload requirements - Benefits of HPE and NVIDIA integrated infrastructure |
| Solution Sizing and Configuration | 17% | - Building configurations via One Config Advanced (OCA) - Differences between configuration sizes and options - 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 |
| Fundamental AI Concepts | 28% | - General AI concepts, applications and workloads - Impact of AI on industries and infrastructure requirements |
| Software Components of HPE Private Cloud AI with NVIDIA | 20% | - Software functions supporting AI operations - Benefits of HPE and NVIDIA software stack |
HPE Private Cloud AI Solutions Sample Questions:
A hospital is developing an AI application to automatically detect specific anomalies in medical images like X-rays and MRIs. The task requires the model to learn and identify complex spatial patterns, such as the shapes and textures of tissues and potential tumors.
Which type of neural network architecture is specifically designed for and best suited to this kind of image analysis task? (Select all that apply.)
- A. A model architecture that includes pooling layers to reduce spatial dimensions
- B. A Convolutional Neural Network (CNN)
- C. A model architecture that includes convolutional layers for feature extraction
- D. A transformer-based model designed for Natural Language Processing (NLP)
- E. A basic, fully connected Artificial Neural Network (ANN)
Correct Answer: A,B,C 🗳️
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 DL320 Gen11 server with NVIDIA L4 GPUs
- C. HPE Cray system with NVIDIA Grace Hopper Superchips
- D. HPE ProLiant DL380a server with NVIDIA H100 NVL GPUs
Correct Answer: B 🗳️
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. 1x HPE ProLiant DL380a Gen11 Server
- B. 2x HPE Aruba Networking CX 6300M Switches
- C. 16x NVIDIA H100 NVL GPUs
- D. 4x HPE ProLiant DL380a Gen11 Servers
- E. 2x NVIDIA SN4700M Switches
- F. 16x NVIDIA L40S GPUs
Correct Answer: B,D,E,F 🗳️
A development team reports that their custom-trained Large Language Model (LLM) is "hallucinating"
- generating factually incorrect or nonsensical information, especially when asked questions outside the scope of its training data. The model was created by fine-tuning a foundation model on a large but static internal dataset. The team wants to improve the model's factual accuracy and reliability without embarking on a new, large-scale training project.
Which are the most appropriate strategies to mitigate this issue? (Choose 2.)
- A. Reduce the "temperature" setting during inference to make the model's output less random and more focused.
- B. Retrain the model from scratch using a much larger and more diverse public dataset.
- C. Apply stricter content moderation and safety guardrails to the model's output.
- D. Implement a Retrieval-Augmented Generation (RAG) framework to provide the model with verifiable, external context at inference time.
- E. Increase the number of hidden layers in the model's architecture.
Correct Answer: A,D 🗳️
An architect is presenting to a customer who is an 'Early AI User'. The customer is unsure about the differences between their two main project ideas.
Project 1: Create an internal chatbot that can answer HR policy questions by accessing the live employee handbook stored on a shared drive.
Project 2: Teach a general-purpose chatbot to adopt the company's formal communication style and tone for drafting official press releases.
How should the architect categorize the primary AI task for each project? (Choose 2.)
- A. Project 2 is a fine-tuning workload.
- B. Project 1 is a RAG (Retrieval-Augmented Generation) workload.
- C. Project 1 is a model training workload.
- D. Project 2 is a classic inferencing workload with no model customization.
Correct Answer: A,B 🗳️
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