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| Section | Objectives |
|---|---|
| HPE Compute Solutions Portfolio | - HPE Synergy and composable infrastructure - HPE ProLiant server solutions - HPE GreenLake compute offerings |
| HPE Compute Architecture Design | - Workload analysis and requirements mapping - Compute infrastructure planning and sizing |
| Operations and Troubleshooting | - Incident resolution and capacity planning - Monitoring and performance optimization |
| Security and Lifecycle Management | - Secure compute architecture principles - Firmware, patching, and lifecycle management |
| Solution Design and Integration | - High availability and scalability design - Storage and networking integration considerations |
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NEW QUESTION # 16
What is one way that NVIDIA Spectrum-X addresses the needs of AI traffic?
Answer: D
Explanation:
NVIDIA Spectrum-X is designed for AI Ethernet fabrics and uses adaptive routing with congestion-control mechanisms to keep traffic balanced, minimize congestion, and reduce tail latency for large AI/RDMA flows.
Reference: https://developer.nvidia.com/blog/optimize-large-scale-ai-workloads-with-nvidia- spectrum-x/
NEW QUESTION # 17
What is one benefit of the NVIDIA AI Enterprise license provided in HPE Private Cloud AI?
Answer: C
Explanation:
The NVIDIA AI Enterprise license in HPE Private Cloud AI provides entitlement to NVIDIA enterprise AI software components used in AI workflows/blueprints, helping customers build and deploy AI applications faster.
Reference: https://docs.nvidia.com/ai-enterprise/software/latest/index.html
https://www.nvidia.com/en-us/data-center/products/ai-enterprise/
NEW QUESTION # 18
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: C
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 # 19
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?
Answer: D
Explanation:
LoRA fine-tuning creates adapter weights, not a new NIM image. NVIDIA NIM supports serving LoRA adapters by setting environment variables such as NIM_PEFT_SOURCE to the NeMo Entity Store/Data Store, allowing the NIM to pull the fine-tuned adapters for inference.
NEW QUESTION # 20
What is a benefit of the storage component of HPE Private Cloud AI (small, medium, and large configurations)?
Answer: A
Explanation:
Small, medium, and large HPE Private Cloud AI configurations use GreenLake for File with Object Storage / Alletra X10000 File and Object Storage, not local node drives. This provides shared, high-performance AI storage for the platform.
Reference: https://www.hpe.com/us/en/collaterals/collateral.a50009216enw.html
NEW QUESTION # 21
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