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HP HPE7-S01 Exam Syllabus Topics:

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

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HP Advanced HPE Compute Architect Solutions Written Exam Sample Questions (Q74-Q79):

NEW QUESTION # 74
A customer asks about the options HPE Private Cloud AI includes out of the box for hyperparameter optimization (HPO) during fine-tuning.
What is one thing that you should explain?

A. Users can run Katib experiments using the built-in Kubeflow framework, which includes several predefined trial templates.
B. Administrators must install a Livy server with which users can run interactive HPO sessions.
C. Users can apply HPO to any training code by uploading the code to the MLflow UI accessible from HPE AI Essentials out of the box.
D. Administrators must expose the built-in Kuberay head service port 10001 externally in order to support Ray Tune.

Answer: A

Explanation:
HPE AI Essentials includes Kubeflow, and Kubeflow includes Katib for hyperparameter tuning experiments. The other options incorrectly map HPO to MLflow, Ray Tune exposure, or Spark/Livy.
Reference:
https://support.hpe.com/hpesc/public/docDisplay?docId=a00ecp55hen_us&docLocale=en_US&p age=reference%2Fkubernetes%2Fusing-kubernetes%2Fgeneral-functionality%2Ftutorials%2Fkubeflow%2FTutorial_Katib_Hyperparameter_Tuning.html
https://www.hpe.com/us/en/collaterals/collateral.a50014263enw.html

NEW QUESTION # 75
Which component, included in HPE Private Cloud AI, can customers use to automate a data pipeline, based on a series of Spark Applications?

A. HPE Machine Learning Inference Software
B. Ray
C. Airflow
D. MLflow

Answer: C

Explanation:
Airflow is used to orchestrate and automate data pipelines as DAGs, including workflows that run a series of Spark Applications. MLIS is for inference, Ray is for distributed compute, and MLflow is for experiment/model tracking.

NEW QUESTION # 76
A government customer requires servers whose components are traceable and assembled under controlled conditions to reduce supply chain risk.
What should you recommend?

A. ProLiant servers with the high-security iLO security state enabled
B. HPE Trusted Supply Chain servers
C. Standard ProLiant servers with TPM 2.0 modules installed
D. Servers ordered with HPE Factory Express Level 4 integration

Answer: B

Explanation:
HPE Trusted Supply Chain servers are built in controlled facilities by vetted personnel, ship with security settings enabled and verified firmware, and provide component traceability. Factory Express provides custom integration but does not address supply chain provenance.

NEW QUESTION # 77
An architect needs to apply a tested, mutually validated set of firmware and drivers to a ProLiant Gen11 fleet.
What should the architect use?

A. HPE Service Pack for ProLiant (SPP) applied with Smart Update Manager (SUM)
B. Redfish firmware inventory export
C. HPE Intelligent Provisioning firmware update wizard
D. Individual component packages downloaded from the HPE Support Center

Answer: A

Explanation:
The SPP is a validated bundle of firmware, drivers, and system software tested together for a given server generation. SUM is the deployment engine that analyzes the target and installs only applicable components in the correct order.

NEW QUESTION # 78
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. Configure the Airflow framework to reference each existing GitHub repo using individual secrets for the credentials.
B. Create a recurring Spark pipeline to copy the DAGs from each GitHub to the Shared Data Volume from which Airflow pulls the DAGs.
C. Set up one Github repo, to which they will refer the Airflow framework, with the existing GitHub repos added as submodules.
D. Replicate the built-in Airflow framework to the namespace of each user with an existing GitHub repo.

Answer: C

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 # 79
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