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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: HPE Compute Architecture and Technologies | 25% | - HPE ProLiant, Apollo and Superdome Flex platforms - Virtualization and container integration - Compute component selection: processors, memory, storage, accelerators |
| Topic 2: Solution Design for Enterprise Workloads | 30% | - Scalability, availability and performance optimization - Design for AI, HPC and mission-critical workloads - Requirements analysis and environment assessment |
| Topic 3: Integration with Data Center Infrastructure | 20% | - HPE OneView management and automation - Networking and storage integration - Security, compliance and data protection |
| Topic 4: Customer Success and Solution Optimization | 10% | - Solution demonstration and value justification - Troubleshooting and continuous improvement |
| Topic 5: Implementation and Lifecycle Management | 15% | - Upgrade, maintenance and monitoring - Deployment planning and validation |
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NEW QUESTION # 68
You are proposing 40 HPE Cray XD670 systems with NVIDIA H200 GPUs to a customer. You also propose HPE Performance Cluster Manager (HPCM).
What is one benefit of HPCM that you should emphasize?
Answer: B
Explanation:
HPE Performance Cluster Manager integrates with NVIDIA DCGM for GPU diagnostics, health, metrics, and alerts, and HPE states it aggregates GPU power monitoring and power capping across the cluster.
Reference: https://www.hpe.com/us/en/collaterals/collateral.a00044858enw.html
https://support.hpe.com/hpesc/public/docDisplay?docId=a00062280en_us&docLocale=en_US&p age=GUID-F340BAA2-EB7C-4270-A2DA-BD54E674452E.html
NEW QUESTION # 69
An HPE Private Cloud AI administrator is running multiple AI workloads and needs insight into which workloads are using resources.
Which HPE Private Cloud AI feature helps to deliver this visibility?
Answer: C
Explanation:
The GPU Control Panel shows workload/framework resource visibility, including assigned vGPUs, pod details, workload status, priority, and GPU utilization charts. This directly helps administrators see which AI workloads are consuming resources.
Reference:
https://support.hpe.com/hpesc/public/docDisplay?docId=a00aie111dhen_us&docLocale=en_US& page=ManageClusters%2Fconfigure-idle-reclaim.html
NEW QUESTION # 70
What is the role of the Retriever Embedding NIM in an NVIDIA NeMo retriever pipeline?
Answer: C
Explanation:
The Retriever Embedding NIM converts ingested text/documents into vector embeddings that can be stored in a vector/RAG database for later retrieval. Matching/ranking retrieved vectors is the role of a reranking/retriever component, not the embedding NIM.
NEW QUESTION # 71
An architect needs to apply a tested, mutually validated set of firmware and drivers to a ProLiant Gen11 fleet.
What should the architect use?
Answer: A
Explanation:
The SPP is a validated bundle of firmware, drivers, and system software tested together for a given server generation. SUM is the deployment engine that analyzes the target and installs only applicable components in the correct order.
NEW QUESTION # 72
A customer has policies requiring the removal of sensitive information from AI fine-tuning data.
Which solution will help them address this need?
Answer: C
Explanation:
NVIDIA NeMo Curator is used for data curation and preprocessing, including PII/sensitive-data identification and removal before data is used for training or fine-tuning. Katib and Ray Tune are for HPO, while NeMo Evaluator evaluates model quality.
Reference: https://docs.nvidia.com/nemo-framework/user-
guide/25.04/datacuration/personalidentifiableinformationidentificationandremoval.html
NEW QUESTION # 73
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