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| Section | Weight | Objectives |
|---|---|---|
| Managing and Operating HPE Private Cloud AI Solutions | 30% | - Describe the tools and methods for managing HPE Private Cloud AI - Explain backup and recovery procedures - Describe how to manage users and access control - Explain how to monitor HPE Private Cloud AI performance and health - Identify troubleshooting procedures and common issues - Identify how to manage storage and data resources |
| Supporting HPE Private Cloud AI Solutions | 20% | - Identify how to perform firmware and software updates - Explain how to work with HPE support services - Describe capacity planning and optimization best practices - Describe support resources and documentation |
| Installing and Configuring HPE Private Cloud AI Solutions | 30% | - Identify the steps to configure the HPE Private Cloud AI environment - Identify how to access and use HPE Private Cloud AI management interfaces - Explain how to deploy and configure HPE Private Cloud AI components - Describe how to validate the HPE Private Cloud AI installation - Describe the prerequisites for installing HPE Private Cloud AI |
| Architecting HPE Private Cloud AI Solutions | 20% | - Explain the HPE Private Cloud AI sizing and configuration guidelines - Describe how HPE Private Cloud AI supports AI/ML workloads - Describe the AI/ML lifecycle and data pipeline requirements - Identify components of the HPE Private Cloud AI architecture - Explain common AI use cases and how they map to workloads |
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NEW QUESTION # 37
What is the primary purpose of using RDMA over Converged Ethernet (RoCE) in the AI interconnect fabric of an HPE Private Cloud AI solution?
Answer: A
NEW QUESTION # 38
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?
Answer: A
NEW QUESTION # 39
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?
Answer: C
NEW QUESTION # 40
An architect is evaluating the high operational costs associated with their company's internal AI platform. The primary workload involves fine-tuning a 70-billion parameter LLM for various departmental tasks. The team reports that the GPU cluster utilization is low, and jobs often fail, requiring manual restarts.
They are reviewing the platform's configuration:
```
- Model: Llama 2 70B
- Task: Supervised Fine-Tuning
- Cluster Size: 16x nodes, each with 4x NVIDIA A100 GPUs
- Scheduling: Manual job submission via SSH scripts
- Data Management: Datasets manually copied to local storage on each node
- Collaboration: Code and models shared via a central Git repository
```
Which NVIDIA AI Enterprise component is specifically designed to address the challenges of low GPU utilization and manual job management in a multi-node training environment like this?
Answer: B
NEW QUESTION # 41
A customer needs a solution for AI inferencing at the edge for a computer vision application. They have determined a Small configuration of HPE Private Cloud AI is sufficient. The solution also requires several value-added services, including an AI transformation workshop and a data design service.
How should an architect attach these services in One Config Advanced (OCA)?
Answer: C
NEW QUESTION # 42
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