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HP HPE2-B08 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Fundamental AI Concepts | 28% | - General AI concepts, applications and workloads - Impact of AI on industries and infrastructure requirements |
| Infrastructure Components of HPE Private Cloud AI with NVIDIA | 20% | - Benefits of HPE and NVIDIA integrated infrastructure - Infrastructure capabilities for AI workload requirements |
| Solution Sizing and Configuration | 17% | - Building configurations via One Config Advanced (OCA) - Using HPE Intelligent Configurator for sizing - Differences between configuration sizes and options |
| Software Components of HPE Private Cloud AI with NVIDIA | 20% | - Software functions supporting AI operations - Benefits of HPE and NVIDIA software stack |
| Customer Assessment and Solution Positioning | 15% | - Position appropriate HPE AI solutions - Assess AI maturity, workload characteristics and use cases |
HPE Private Cloud AI Solutions Sample Questions:
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 has selected the "HPE Private Cloud AI - Medium - Expanded" configuration.
Which of the following resource counts correctly describes this specific configuration?
- A. 4 worker nodes, 16 total H100 NVL GPUs, dual rack
- B. 8 worker nodes, 32 total H100 NVL GPUs, dual rack
- C. 2 worker nodes, 8 total L40S GPUs, single rack
- D. 4 worker nodes, 16 total L40S GPUs, single rack
Correct Answer: D 🗳️
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 difficulty of managing GPU cluster utilization for training jobs.
- B. The complexity of writing Python code in a Jupyter Notebook.
- 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.
Correct Answer: C 🗳️
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. Retrain the model from scratch using a much larger and more diverse public dataset.
- B. Increase the number of hidden layers in the model's architecture.
- C. Reduce the "temperature" setting during inference to make the model's output less random and more focused.
- D. Apply stricter content moderation and safety guardrails to the model's output.
- E. Implement a Retrieval-Augmented Generation (RAG) framework to provide the model with verifiable, external context at inference time.
Correct Answer: C,E 🗳️
A customer at the 'Early AI User' maturity stage wants to begin using generative AI. Their primary concern is the complexity of deploying and managing the AI software stack. They are looking for a solution that accelerates their journey by providing a pre-integrated, enterprise-supported software platform for both development and deployment.
How do the software components of HPE Private Cloud AI address this customer's main challenge?
(Select all that apply.)
```
Customer Profile:
- Maturity: Early AI User
- Main Challenge: Complexity of AI software stack
- Goal: Accelerate adoption of generative AI
```
- A. NVIDIA NIMs drastically simplify the deployment of pre-trained models into production-ready microservices.
- B. HPE AI Essentials provides a unified, self-service platform with curated open-source tools, eliminating integration complexity.
- C. The software stack is limited to only HPE-developed tools to ensure a single point of contact.
- D. The solution requires the customer to manually integrate all software components, which promotes learning.
- E. NVIDIAAI Enterprise provides enterprise-grade support, security, and API stability for the entire AI software stack.
Correct Answer: A,B,E 🗳️
