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HP HPE7-S01 Exam Syllabus Topics:

HP Advanced HPE Compute Architect Solutions Written Sample Questions:

1. An organization is planning to run AI inferencing for customer service. The organization has strict environmental efficiency standards. They have facility water access at the rack.
Which server configuration should you recommend?

A) HPE ProLiant DL580 Gen 12 with liquid-to-air rear-door cooling
B) HPE ProLiant DL380 Gen12 with high-performance heat sinks
C) HPE ProLiant DL380a Gen12 with 70% DLC
D) HPE ProLiant DL360 Gen12 with closed-loop liquid cooling

2. A VMware customer wants firmware and driver updates for ProLiant hosts to be orchestrated by vSphere Lifecycle Manager together with ESXi updates.
What must be deployed on the hosts?

A) HPE OneView for VMware vCenter in monitoring-only mode
B) HPE Agentless Management Service only
C) Smart Update Manager installed in a management virtual machine
D) HPE Integrated Smart Update Tools (iSUT) with the HPE hardware support manager registered

3. A customer's rack has a hard power budget, and the architect must guarantee that a group of servers never exceeds a defined total wattage.
What should you recommend?

A) Configure Static Low Power Mode in each server's BIOS
B) Reduce processor core counts in the configuration
C) Configure dynamic power capping through iLO or HPE OneView for the server group
D) Set the HPE Power Regulator to OS Control Mode

4. 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?

A) Replace RDIMMs with LRDIMMs of the same capacity
B) Set the memory operating speed manually to the maximum supported value
C) Enable Memory Interleaving in the UEFI workload profile
D) Populate DIMMs so that all memory channels per processor are equally populated

5. An HPE Private Cloud AI customer needs help planning how to use Airflow within that solution.
The customer currently has several GitHub repos in which different teams store their DAGs.
What should you advise AI administrators to do?

A) Create a recurring Spark pipeline to copy the DAGs from each GitHub to the Shared Data Volume from which Airflow pulls the DAGs.
B) Set up one Github repo, to which they will refer the Airflow framework, with the existing GitHub repos added as submodules.
C) Configure the Airflow framework to reference each existing GitHub repo using individual secrets for the credentials.
D) Replicate the built-in Airflow framework to the namespace of each user with an existing GitHub repo.

Solutions: