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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Integration with Data Center Infrastructure | 20% | - HPE OneView management and automation - Security, compliance and data protection - Networking and storage integration |
| Topic 2: Customer Success and Solution Optimization | 10% | - Troubleshooting and continuous improvement - Solution demonstration and value justification |
| Topic 3: Solution Design for Enterprise Workloads | 30% | - Design for AI, HPC and mission-critical workloads - Scalability, availability and performance optimization - Requirements analysis and environment assessment |
| Topic 4: Implementation and Lifecycle Management | 15% | - Deployment planning and validation - Upgrade, maintenance and monitoring |
| Topic 5: HPE Compute Architecture and Technologies | 25% | - Virtualization and container integration - Compute component selection: processors, memory, storage, accelerators - HPE ProLiant, Apollo and Superdome Flex platforms |
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NEW QUESTION # 34
A customer has developed a custom NIM which they store in their NGC Private Registry. The customer would like to use that NIM in inferencing workloads running on HPE Private Cloud AI.
What should you recommend?
Answer: D
Explanation:
HPE Private Cloud AI uses HPE MLIS to deploy NIM-based inference services, and MLIS supports configuring an NGC registry so it can pull NIMs from NVIDIA NGC/private registry sources. Manual KServe or Ray Serve deployment is unnecessary for this use case.
Reference:
https://support.hpe.com/hpesc/public/docDisplay?docId=a00aie111dhen_us&docLocale=en_US& page=pcai%2FCatalogs%2Fdeploy-NIM-using-s3.html
NEW QUESTION # 35
You are demonstrating HPE Private Cloud AI and HPE Machine Learning Inference Software to a customer. The customer says that the organization has a custom model, stored in an OpenLLM hub, which they would like to deploy with HPE Machine Learning Inference Software.
What should you explain?
Answer: C
Explanation:
MLIS supports OpenLLM registries, which allow it to pull OpenLLM-compatible models directly from the Hugging Face/OpenLLM hub and use them to create deployments.
Reference:
https://support.hpe.com/hpesc/public/docDisplay?docId=a00aie112hen_us&docLocale=en_US&p age=MLIS%2Fregistries%2Fopenllm.html
NEW QUESTION # 36
A customer plans to train large language models on premises and requires eight interconnected GPUs per node with high GPU-to-GPU bandwidth.
Which platform should you recommend?
Answer: C
Explanation:
HPE ProLiant Compute XD systems are purpose-built for large-scale AI training, supporting dense eight-way GPU configurations with high-speed GPU interconnect and high-bandwidth fabric adapters. Standard 1U dual-GPU servers cannot deliver the required GPU density or interconnect bandwidth.
NEW QUESTION # 37
A customer is evaluating whether to run their AI proof of concept (PoC) in the cloud or on-prem.
They store sensitive data and require low-latency performance.
Which factor supports running the PoC in the data center?
Answer: C
Explanation:
Keeping the PoC in the data center supports low-latency access to local sensitive data and can reduce data movement/compliance concerns compared with sending data to the cloud.
NEW QUESTION # 38
A retail customer has 400 ProLiant DL servers distributed across store locations with no local IT staff and no management appliance on site.
Which HPE solution should you recommend for firmware lifecycle management?
Answer: B
Explanation:
HPE Compute Ops Management is a cloud-based service in the HPE GreenLake platform that provides firmware baseline management, monitoring, and reporting for distributed servers without requiring on-premises management infrastructure.
NEW QUESTION # 39
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