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HP HPE7-S01 Exam Syllabus Topics:

SectionObjectives
Topic 1: Operations and Troubleshooting- Incident resolution and capacity planning
- Monitoring and performance optimization
Topic 2: Solution Design and Integration- Storage and networking integration considerations
- High availability and scalability design
Topic 3: HPE Compute Solutions Portfolio- HPE GreenLake compute offerings
- HPE Synergy and composable infrastructure
- HPE ProLiant server solutions
Topic 4: Security and Lifecycle Management- Secure compute architecture principles
- Firmware, patching, and lifecycle management
Topic 5: HPE Compute Architecture Design- Compute infrastructure planning and sizing
- Workload analysis and requirements mapping

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HP Advanced HPE Compute Architect Solutions Written Exam Sample Questions (Q48-Q53):

NEW QUESTION # 48
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: C

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 # 49
A customer plans to use Ray with HPE Private Cloud AI and wants to know more about the framework's integration with the solution's GPUs.
What should you explain?

Answer: A

Explanation:
Ray GPU support creates a separate GPU worker pod/group for GPU-accelerated Ray jobs, and administrators can configure its GPU resources; this GPU worker is not autoscaled.
Reference: https://docs.ezmeral.hpe.com/unified-analytics/15/Ray/ray-gpu-support.html


NEW QUESTION # 50
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: A,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 # 51
An HPE Synergy customer wants compute modules to boot a bare-metal OS image with no OS installed on local disks, so that hardware can be repurposed within minutes.
Which component provides this capability?

Answer: A

Explanation:
HPE Synergy Image Streamer provides stateless, image-based OS deployment. Golden images and deployment plans are applied through server profiles, allowing compute modules to boot from the Image Streamer appliance instead of local storage.


NEW QUESTION # 52
A customer must decommission ProLiant Gen11 servers and needs to sanitize all internal drives, NVRAM, and iLO settings before the servers leave the data center.
What should you recommend?

Answer: C

Explanation:
One-button secure erase in iLO performs a NIST SP 800-88 compliant sanitization of supported drives, non-volatile memory, and iLO/BIOS settings, and produces an erase report for audit.
Deleting logical drives or reflashing firmware does not remove user data.


NEW QUESTION # 53
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