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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Managing and Operating HPE Private Cloud AI Solutions | 30% | - Explain how to monitor HPE Private Cloud AI performance and health - Describe the tools and methods for managing HPE Private Cloud AI - Identify how to manage storage and data resources - Identify troubleshooting procedures and common issues - Describe how to manage users and access control - Explain backup and recovery procedures |
| Topic 2: Architecting HPE Private Cloud AI Solutions | 20% | - Explain the HPE Private Cloud AI sizing and configuration guidelines - Describe the AI/ML lifecycle and data pipeline requirements - Identify components of the HPE Private Cloud AI architecture - Describe how HPE Private Cloud AI supports AI/ML workloads - Explain common AI use cases and how they map to workloads |
| Topic 3: 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 |
| Topic 4: Installing and Configuring HPE Private Cloud AI Solutions | 30% | - Explain how to deploy and configure HPE Private Cloud AI components - Describe the prerequisites for installing HPE Private Cloud AI - Identify the steps to configure the HPE Private Cloud AI environment - Identify how to access and use HPE Private Cloud AI management interfaces - Describe how to validate the HPE Private Cloud AI installation |
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NEW QUESTION # 75
An organization is using HPE Private Cloud AI to manage a large, multi-tenant Kubernetes environment for its data science teams. They need to ensure that data access is securely managed and that each team's data is isolated within a logical, policy-driven boundary. Additionally, they need to store and access data from multiple sources, including NFS clients and S3-compatible object stores.
Which software components of the HPE Private Cloud AI stack work together to provide this unified, policy-driven, multi-protocol data access? (Select all that apply.)
Answer: A,C
NEW QUESTION # 76
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: B
NEW QUESTION # 77
An architect is designing an infrastructure solution for an AI workload that involves processing massive datasets for training. The goal is to minimize data transfer latency between the storage system and the GPUs in the compute nodes. The architect wants to enable the NVIDIA GPUs to fetch data directly from the NVMe storage array, bypassing the server's CPU and main memory.
Review the proposed architectural components:
```
- Compute Nodes: HPE ProLiant DL380a Gen11 with NVIDIA H100 GPUs
- Storage: HPE GreenLake for File Storage
- Interconnect: Ethernet with RoCE support
```
Which technology must be enabled and properly configured across these components to achieve this direct GPU-to-storage data path? (Choose 2.)
Answer: C,E
NEW QUESTION # 78
A customer in the media and entertainment industry wants a solution to accelerate the creation of 3D animations and renderings. Their artists need to be able to generate complex visual AI content quickly.
The company has a mature AI practice and requires a powerful, data center-based solution.
Which HPE ProLiant server and NVIDIA GPU combination is purpose-built for these generative visual AI workloads?
Answer: B
NEW QUESTION # 79
A customer has selected the "HPE Private Cloud AI - Medium - Expanded" configuration.
Which of the following resource counts correctly describes this specific configuration?
Answer: A
NEW QUESTION # 80
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