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| Section | Weight | Objectives |
|---|---|---|
| HPE Compute Architecture and Technologies | 25% | - Compute component selection: processors, memory, storage, accelerators - Virtualization and container integration - HPE ProLiant, Apollo and Superdome Flex platforms |
| Customer Success and Solution Optimization | 10% | - Troubleshooting and continuous improvement - Solution demonstration and value justification |
| Solution Design for Enterprise Workloads | 30% | - Scalability, availability and performance optimization - Design for AI, HPC and mission-critical workloads - Requirements analysis and environment assessment |
| Implementation and Lifecycle Management | 15% | - Deployment planning and validation - Upgrade, maintenance and monitoring |
| Integration with Data Center Infrastructure | 20% | - Networking and storage integration - Security, compliance and data protection - HPE OneView management and automation |
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NEW QUESTION # 56
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?
Answer: A
Explanation:
HPE AI Essentials inference endpoints use access tokens for remote/client access, so the web application must pass the deployment's API token in the request headers when querying the NIM endpoint.
Reference:
https://support.hpe.com/hpesc/public/docDisplay?docId=a00aie18hen_us&docLocale=en_US&pa ge=pcai%2FGenAI%2Finfer-model-endpoint.html&
NEW QUESTION # 57
A customer reports lower-than-expected memory bandwidth on a two-socket ProLiant Gen11 server populated with 12 DIMMs per socket in an unbalanced pattern.
What should you recommend?
Answer: D
Explanation:
DDR5 platforms achieve maximum bandwidth only when every memory channel of a processor is populated identically. Unbalanced population disables interleaving across channels and reduces effective bandwidth regardless of BIOS settings.
NEW QUESTION # 58
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?
Answer: C
Explanation:
AI readiness depends on understanding where data comes from, who owns it, how it is formatted, and how it moves between systems. This establishes whether the organization can support accurate, secure, and scalable AI deployment.
NEW QUESTION # 59
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 # 60
A financial services customer runs a market data application that is extremely sensitive to jitter and response time, not throughput.
Which UEFI workload profile should you recommend?
Answer: A
Explanation:
The Low Latency workload profile disables processor C-states and other power management features and tunes settings to minimize jitter and deterministic response time, at the cost of higher power consumption.
NEW QUESTION # 61
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