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HP HPE7-S01 Exam Syllabus Topics:

Section	Weight	Objectives
Demonstrate AI Solutions	10%	- AI solution demonstration 1. Customer use cases 2. Business value presentation 3. Solution positioning
Manage Implementation and Startup of HPE AI and HPC Solutions	2%	- Deployment and validation 1. Implementation planning 2. Operational readiness 3. Configuration verification
Explain HPE Compute AI and HPC Solution Components and Architecture	38%	- AI and HPC architectures 1. High-performance workload platforms 2. GPU-accelerated computing 3. NVIDIA integration - HPE Compute portfolio 1. HPE ProLiant servers 2. HPE Cray systems 3. HPE Synergy - Management and automation 1. HPE GreenLake 2. Lifecycle management 3. HPE OneView
Design HPE Solutions for AI and HPC Workloads	50%	- Solution architecture and design 1. Capacity planning 2. Workload analysis 3. Performance optimization - Infrastructure integration 1. Hybrid cloud architectures 2. Compute, storage, and networking integration 3. Scalability and resiliency design

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HP Advanced HPE Compute Architect Solutions Written Exam Sample Questions (Q80-Q85):

NEW QUESTION # 80
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. Configure the Airflow framework to reference each existing GitHub repo using individual secrets for the credentials.
C. Replicate the built-in Airflow framework to the namespace of each user with an existing GitHub repo.
D. Set up one Github repo, to which they will refer the Airflow framework, with the existing GitHub repos added as submodules.

Answer: D

Explanation:
HPE AI Essentials Airflow is configured to pull DAGs from a specified Git repository, branch, and subdirectory. For multiple team DAG repositories, the practical approach is to point Airflow to one parent repo and include the other repos as Git submodules.
Reference:
https://support.hpe.com/hpesc/public/docDisplay?docId=a00aie111hen_us&docLocale=en_US&p age=Airflow%2Fairflow-dags-git-repos.html

NEW QUESTION # 81
A customer is considering HPE Private Cloud AI for an AI PoC and needs to choose between a small configuration and a Developer System configuration.
What is one difference that you should explain?

A. The Developer System runs entirely in the cloud, as opposed to having on-prem hardware.
B. The Developer System has a reduced set of software tools and capabilities compared to the small configuration.
C. The Developer System uses less powerful L4 GPUs, as opposed to L40S GPUs.
D. The Developer System uses integrated storage rather than GreenLake for File Storage.

Answer: D

Explanation:
HPE Private Cloud AI Developer System is a smaller PoC-oriented configuration with internal file/object storage, while the small configuration uses external HPE Alletra X10000 / GreenLake for File with Object Storage.
Reference: https://www.hpe.com/us/en/collaterals/collateral.a50009216enw.html

NEW QUESTION # 82
A customer is interested in HPE Private Cloud AI's Data Lakehouse Gateway and needs help determining whether this gateway will work with their data lakehouse.
What is one requirement to explain?

A. The data lakehouse must be hosted in AWS.
B. The data lakehouse must use ARVO files.
C. The data lakehouse must be deployed on S3 or NFSv4 storage.
D. The data lakehouse must provide native integration with EzPresto.

Answer: C

Explanation:
The Data Lakehouse Gateway is not AWS-only. HPE states it supports data lakehouses on AWS S3, NFSv4, and S3-compatible storage, so the best requirement is S3 or NFSv4 storage.
Reference:
https://support.hpe.com/hpesc/public/docDisplay?docId=a00aie111hen_us&docLocale=en_US&p age=EzPresto%2Fconnecting-data-lakehouses.html

NEW QUESTION # 83
What is one reason to suggest a VMware vSAN OSA deployment rather than an ESA deployment?

A. The customer wants to create a vSAN cluster that provides storage for other clusters but does not host VMs.
B. The customer wants to use features such as compression, which are not supported with ESA.
C. The customer wants a cost-effective solution that incorporates HDDs.
D. The customer prioritizes obtaining the highest write performance.

Answer: C

Explanation:
vSAN OSA supports hybrid configurations with SSD cache and HDD capacity drives, while ESA is optimized for modern all-flash/NVMe hardware and is the higher-performance architecture.
Reference: https://techdocs.broadcom.com/us/en/vmware-cis/vsan/vsan/8-0/vsan- administration/building-a-virtual-san-cluster.html

NEW QUESTION # 84
A customer has policies requiring the removal of sensitive information from AI fine-tuning data.
Which solution will help them address this need?

A. NVIDIA NeMo Evaluator
B. Ray Tune
C. Kubeflow Katib
D. NVIDIA NeMo Curator

Answer: D

Explanation:
NVIDIA NeMo Curator is used for data curation and preprocessing, including PII/sensitive-data identification and removal before data is used for training or fine-tuning. Katib and Ray Tune are for HPO, while NeMo Evaluator evaluates model quality.
Reference: https://docs.nvidia.com/nemo-framework/user-
guide/25.04/datacuration/personalidentifiableinformationidentificationandremoval.html

NEW QUESTION # 85
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