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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 the HPE Private Cloud AI sizing and configuration guidelines - Explain common AI use cases and how they map to workloads - Describe how HPE Private Cloud AI supports AI/ML workloads - Identify components of the HPE Private Cloud AI architecture |
| Installing and Configuring HPE Private Cloud AI Solutions | 30% | - Describe how to validate the HPE Private Cloud AI installation - 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 the prerequisites for installing HPE Private Cloud AI |
| Managing and Operating HPE Private Cloud AI Solutions | 30% | - Explain how to monitor HPE Private Cloud AI performance and health - Explain backup and recovery procedures - Identify troubleshooting procedures and common issues - Identify how to manage storage and data resources - Describe how to manage users and access control - 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 - Describe support resources and documentation - Explain how to work with HPE support services - Identify how to perform firmware and software updates |
HPE Private Cloud AI Solutions Sample Questions:
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. The inclusion of a full-stack, enterprise-supported software suite including NVIDIA AI Enterprise and HPE AI Essentials.
- B. Its turnkey nature, which simplifies deployment and allows their expert team to focus on building models rather than integrating infrastructure.
- C. The option to choose a Large configuration with powerful NVIDIA H100 GPUs and a high-speed RoCE fabric for demanding fine-tuning workloads.
- D. Its ability to run on a single, low-power server for maximum efficiency.
- E. A simple, web-based interface for beginners to learn about AI concepts.
A large financial institution, a known "Deployer of AI at scale," needs to train a next-generation fraud detection model. This new model has over a trillion parameters, significantly larger than their current models, and requires an exascale-class computing solution to be trained in a reasonable timeframe.
Which HPE AI solution should be positioned to meet this customer's demanding requirement?
- A. HPE GreenLake for File Storage
- B. HPE Cray systems
- C. HPE Private Cloud AI with NVIDIA, Large configuration
- D. AI-optimized HPE ProLiant DL servers
After an architect selects the "HPE Private Cloud AI - Large - Expanded" Smart Template in OCA, they see it includes a significant number of "HPE Factory Express Complex Unit of SVC" services.
What is the purpose of these bundled services?
- A. To provide on-site training for the customer's data science team.
- B. To cover the pre-integration and configuration of the entire solution at an HPE factory before shipment.
- C. To allow the customer to exchange the hardware for the next generation at no cost.
- D. To provide a credit for future software purchases on the HPE GreenLake cloud.
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. Retrieval-Augmented Generation (RAG)
- B. AI Inferencing
- C. AI Model Training / Fine-tuning
- D. Edge Computing
An architect uses the HPE Intelligent Configurator for a customer with 120 concurrent users for a text generation task with RAG. The tool recommends a "Medium - Expanded (4-node)" configuration. The customer then reveals they want to use a smaller, more efficient 7B parameter model instead of the 13B model the tool defaulted to.
How will this change in model size likely affect the sizing tool's recommendation?
- A. It will downgrade the recommendation to a "Medium - Standard (2-node)" configuration.
- B. It will require the architect to switch to the "application capacity" sizing method.
- C. It will upgrade the recommendation to a "Large - Standard (4-node)" configuration.
- D. It will have no effect, as the number of users is the primary factor.
