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| Section | Objectives |
|---|---|
| Topic 1: HPE Compute Solutions Portfolio | - HPE ProLiant server solutions - HPE Synergy and composable infrastructure - HPE GreenLake compute offerings |
| Topic 2: HPE Compute Architecture Design | - Compute infrastructure planning and sizing - Workload analysis and requirements mapping |
| Topic 3: Solution Design and Integration | - Storage and networking integration considerations - High availability and scalability design |
| Topic 4: Security and Lifecycle Management | - Secure compute architecture principles - Firmware, patching, and lifecycle management |
| Topic 5: Operations and Troubleshooting | - Incident resolution and capacity planning - Monitoring and performance optimization |
1. You are setting up a demonstration of HPE Private Cloud AI. You will show how easy it is to deploy a NIM with HPE Machine Learning Inference Software. As part of the demonstration, you worked with a developer to create a simple web application that sends queries to this NIM.
What is one important thing to keep in mind about your plan?
A) You will need to set up the application to include an API token, associated with the deployment, in the headers of requests.
B) You must set up a storageclass that references HPE GreenLake for File Storage in advance to ensure that the deployment proceeds correctly.
C) You must develop an API that understands the application's queries and incorporates those in the NIM as a custom image.
D) You must establish a private NGC registry and collect its credentials to prepare for defining the source in HPE Machine Learning Inference Software.
2. Which factors should you consider when determining the GPU memory requirements for inferencing an AI model?
A) The number of parameters in the model and the floating point precision
B) The memory available on the host and the number of parameters in the model
C) The memory available on the host and the number of convolutional layers in the model
D) The floating point precision and the number of layers in the model
3. A customer plans to implement machine learning models but has limited visibility into where business data is generated and how it is managed across departments.
During the readiness assessment, which step will best support an accurate evaluation of their AI deployment potential?
A) Begin by installing a standard data lake connector and mapping legacy tables to object storage.
B) Focus the session on reviewing backup retention schedules and recovery point objectives.
C) Identify data sources, formats, ownership, and movement patterns across the organization.
D) Recommend bypassing initial workshops to begin with a small AI pilot on anonymized data.
4. An HPE Synergy customer is expanding from one frame to seven frames and wants to minimize the number of interconnect modules and uplink ports required.
Which design should you recommend?
A) Master Virtual Connect modules in two frames with satellite modules in the remaining frames
B) Pass-through modules in all frames connected to top-of-rack switches
C) Direct-attach SAS interconnects in each frame with shared uplinks
D) A pair of Virtual Connect modules in every frame, each with its own uplinks
5. A customer is considering HPE Private Cloud AI for an AI PoC and needs to choose between a small configuration and a Developer System configuration.
What is one difference that you should explain?
A) The Developer System uses integrated storage rather than GreenLake for File Storage.
B) The Developer System runs entirely in the cloud, as opposed to having on-prem hardware.
C) The Developer System uses less powerful L4 GPUs, as opposed to L40S GPUs.
D) The Developer System has a reduced set of software tools and capabilities compared to the small configuration.
Solutions:
| Question # 1 Answer: A | Question # 2 Answer: A | Question # 3 Answer: C | Question # 4 Answer: A | Question # 5 Answer: A |
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