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HP HPE7-S01 Exam Syllabus Topics:

SectionWeightObjectives
Manage Implementation and Startup of HPE AI and HPC Solutions2%- Deployment and validation
  • 1. Configuration verification
  • 2. Implementation planning
  • 3. Operational readiness
Design HPE Solutions for AI and HPC Workloads50%- Infrastructure integration
  • 1. Hybrid cloud architectures
  • 2. Compute, storage, and networking integration
  • 3. Scalability and resiliency design
- Solution architecture and design
  • 1. Workload analysis
  • 2. Capacity planning
  • 3. Performance optimization
Demonstrate AI Solutions10%- AI solution demonstration
  • 1. Customer use cases
  • 2. Business value presentation
  • 3. Solution positioning
Explain HPE Compute AI and HPC Solution Components and Architecture38%- AI and HPC architectures
  • 1. High-performance workload platforms
  • 2. NVIDIA integration
  • 3. GPU-accelerated computing
- HPE Compute portfolio
  • 1. HPE Cray systems
  • 2. HPE ProLiant servers
  • 3. HPE Synergy
- Management and automation
  • 1. Lifecycle management
  • 2. HPE OneView
  • 3. HPE GreenLake

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HP Advanced HPE Compute Architect Solutions Written Exam Sample Questions (Q37-Q42):

NEW QUESTION # 37
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.)

Answer: C,D

Explanation:
KServe autoscaling adjusts inference deployment capacity based on demand. HPE AI Essentials scheduler priorities ensure higher-priority inference workloads can get GPUs ahead of lower- priority notebook workloads.
Reference: https://kserve.github.io/website/docs/model-serving/predictive- inference/autoscaling/hpa-autoscaler
https://support.hpe.com/hpesc/public/docDisplay?docId=a00aie17hen_us&docLocale=en_US&pa ge=pcai%2FGenAI%2Fdeploying-knowledge-base.html


NEW QUESTION # 38
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 # 39
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: A

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 # 40
A customer wants you to design a solution for effectively near 100% liquid cooling for their HPE Cray XD systems.
Which solution should you propose?

Answer: B

Explanation:
HPE Cray XD DLC removes the main component heat, while HPE RDHX captures the remaining hot exhaust air and transfers it to facility water, enabling effectively near-100% heat removal to liquid cooling.
Reference: https://www.hpe.com/us/en/collaterals/collateral.a50004292enw.html


NEW QUESTION # 41
Which statement correctly describes hardware security capabilities for HPE Cray XD systems?

Answer: C

Explanation:
HPE Cray XD systems use an HPE Cray XD BMC that provides hardware root of trust, recovery, chain of trust, and secure boot capabilities. They do not use the same iLO ASIC/firmware as HPE ProLiant DL servers.
Reference: https://www.hpe.com/us/en/collaterals/collateral.a50004301enw.html


NEW QUESTION # 42
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