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HP HPE7-S01 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Explain HPE Compute AI and HPC Solution Components and Architecture38%- AI and HPC architectures
  • 1. NVIDIA integration
  • 2. GPU-accelerated computing
  • 3. High-performance workload platforms
- HPE Compute portfolio
  • 1. HPE Cray systems
  • 2. HPE Synergy
  • 3. HPE ProLiant servers
- Management and automation
  • 1. HPE OneView
  • 2. HPE GreenLake
  • 3. Lifecycle management
Topic 2: Manage Implementation and Startup of HPE AI and HPC Solutions2%- Deployment and validation
  • 1. Configuration verification
  • 2. Implementation planning
  • 3. Operational readiness
Topic 3: Design HPE Solutions for AI and HPC Workloads50%- Solution architecture and design
  • 1. Performance optimization
  • 2. Workload analysis
  • 3. Capacity planning
- Infrastructure integration
  • 1. Scalability and resiliency design
  • 2. Hybrid cloud architectures
  • 3. Compute, storage, and networking integration
Topic 4: Demonstrate AI Solutions10%- AI solution demonstration
  • 1. Solution positioning
  • 2. Business value presentation
  • 3. Customer use cases

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HP Advanced HPE Compute Architect Solutions Written Exam Sample Questions (Q61-Q66):

NEW QUESTION # 61
A customer requires an HPE Cray Storage Systems C500 solution with an SMU and a fully populated C500 CSU using 6.4TB drives. The customer needs to add at least 64TB of raw flash capacity.
What should you recommend?

Answer: D

Explanation:
The customer already has the required SMU. To add flash capacity, use an SSU-F, not a 5U84 HDD enclosure. A half-populated SSU-F with 12 × 6.4 TB SSDs provides 76.8 TB raw, meeting the 64 TB requirement.


NEW QUESTION # 62
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: B,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 # 63
You want to demonstrate HPE Private Cloud AI running a complex AI application, such as a video search app, to a customer.
What is one good resource for putting together this demonstration more quickly?

Answer: C

Explanation:
NVIDIA AI Blueprints are predefined, customizable reference workflows/sample AI applications designed to accelerate building and demonstrating complex AI use cases. HPE AI Essentials specifically describes Blueprints as workflows for creating and deploying generative AI applications.
Reference:
https://support.hpe.com/hpesc/public/docDisplay?docId=sd00007592en_us&docLocale=en_US& page=GUID-7899509F-3811-4AB7-9528-DCD0A948F4FE.html


NEW QUESTION # 64
A customer runs a transactional database with a write-intensive I/O profile on twelve SAS SSDs attached to an HPE MR controller and requires fault tolerance with minimal write penalty.
Which RAID level should you recommend?

Answer: A

Explanation:
RAID 10 mirrors and stripes, incurring a write penalty of 2 compared with 4 for RAID 5 and 6 for RAID 6. For write-heavy transactional workloads where usable capacity is secondary to write performance, RAID 10 is the appropriate choice.


NEW QUESTION # 65
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: A

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 # 66
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