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HP HPE7-S01 Exam Syllabus Topics:

HP Advanced HPE Compute Architect Solutions Written Sample Questions:

1. An automotive engineering team is running inference workloads for traffic simulation and computer vision models using KServe on HPE Private Cloud AI.
The team also uses GPU-enabled notebooks running on HPE Private Cloud AI to experiment with new models. They want to automatically adjust GPU usage for the different models based on current demand, as well as ensure that the notebooks never prevent the inferencing workloads from receiving the GPUs they need.
Which two features support this functionality? (Choose two.)

A) Kyverno mutating admission policies
B) Canary roll outs for KServe
C) Autoscaling for KServe
D) HPE AI Essentials scheduler priorities

2. A customer wants to understand more about how HPE AI Essentials secures communications between pods running on HPE AI Essentials.
Which correctly describes how these pods authenticate each other when establishing TLS communications?

A) Using certificates issued by the HPE GreenLake identity provider
B) Using certificates signed by the built-in Istio gateway
C) Using tokens obtained from the built-in Keycloak
D) Using SVIDs obtained from the built-in SPIRE server

3. A customer has policies requiring the removal of sensitive information from AI fine-tuning data.
Which solution will help them address this need?

A) NVIDIA NeMo Curator
B) Ray Tune
C) NVIDIA NeMo Evaluator
D) Kubeflow Katib

4. An HPE Synergy customer wants compute modules to boot a bare-metal OS image with no OS installed on local disks, so that hardware can be repurposed within minutes.
Which component provides this capability?

A) HPE Image Streamer
B) HPE Synergy Composer
C) HPE OneView Global Dashboard
D) HPE Intelligent Provisioning

5. A customer has an HPE Private Cloud AI solution, running HPE AI Essentials 1.11. The customer has identified a NIM for an LLM that they want to use. The customer has imported NeMo microservices on the AI Essentials platform. The customer plans to use NeMo Customizer's LoRA to fine-tune the LLM for their purposes.
What should you recommend that the customer do to deploy the fine-tuned model on HPE Private Cloud AI with HPE Machine Learning Inference Software?

A) Use environment variables when they deploy the model to point to the NeMo entity store running on AI Essentials.
B) Set up a custom OpenLLM registry in which they add the fine-tuned weights.
C) Store the fine-tuned weights in the local S3 object store.
D) Submit a support request to add the fine-tuned model to the NIMs hosted on HPE Private Cloud AI.

Solutions: