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

HP Advanced HPE Compute Architect Solutions Written Sample Questions:

1. You are meeting with the AI project team at Company PXXX for the first time. They have invited IT staff, sales leaders, and back-office representatives.
What is the best way to begin evaluating their environment?

A) Schedule one-on-one follow-up meetings to avoid cross-functional input.
B) Ask each stakeholder group to describe the data dependencies.
C) Present potential HPE configurations and walk through the quote process.
D) Focus on available GPU inventory and calculate maximum server density.


2. 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?

A) 100% DLC for the HPE Cray XD systems
B) Closed-loop liquid cooling for the HPE Cray XD Systems plus an HPE ARCS
C) 70% DLC plus a closed-loop liquid cooling system for the HPE Cray XD Systems
D) 70% DLC for the HPE Cray XD systems plus an HPE RDHX


3. 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?

A) Low Latency
B) General Peak Frequency Compute
C) Graphic Processing
D) Virtualization - Max Performance


4. 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?

A) Downloading the custom NIM to individual users' models-pvc and manually creating KServe endpoints to deploy the NIM
B) Using MLIS with an NGC registry that points to the customer's private registry
C) Finding and importing NVIDIA AI blueprint that supports the custom NIM
D) Using Ray Serve with custom-built deployments based on the custom NIM image


5. 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.)

A) Canary roll outs for KServe
B) Kyverno mutating admission policies
C) Autoscaling for KServe
D) HPE AI Essentials scheduler priorities


Solutions:

Question # 1
Answer: B
Question # 2
Answer: A
Question # 3
Answer: A
Question # 4
Answer: B
Question # 5
Answer: C,D

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