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HP HPE7-S01 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Implementation and Lifecycle Management	15%	- Deployment planning and validation - Upgrade, maintenance and monitoring
Topic 2: Customer Success and Solution Optimization	10%	- Troubleshooting and continuous improvement - Solution demonstration and value justification
Topic 3: Solution Design for Enterprise Workloads	30%	- Scalability, availability and performance optimization - Requirements analysis and environment assessment - Design for AI, HPC and mission-critical workloads
Topic 4: Integration with Data Center Infrastructure	20%	- Security, compliance and data protection - HPE OneView management and automation - Networking and storage integration
Topic 5: HPE Compute Architecture and Technologies	25%	- Virtualization and container integration - HPE ProLiant, Apollo and Superdome Flex platforms - Compute component selection: processors, memory, storage, accelerators

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HP Advanced HPE Compute Architect Solutions Written Exam Sample Questions (Q53-Q58):

NEW QUESTION # 53
An HPE Private Cloud AI administrator is running multiple AI workloads and needs insight into which workloads are using resources.
Which HPE Private Cloud AI feature helps to deliver this visibility?

A. HPE GreenLake Cloud AI Insights
B. HPE Private Cloud AI system view
C. HPE AI Essentials GPU control panel
D. AI optimized nodes' iLO interfaces

Answer: C

Explanation:
The GPU Control Panel shows workload/framework resource visibility, including assigned vGPUs, pod details, workload status, priority, and GPU utilization charts. This directly helps administrators see which AI workloads are consuming resources.
Reference:
https://support.hpe.com/hpesc/public/docDisplay?docId=a00aie111dhen_us&docLocale=en_US& page=ManageClusters%2Fconfigure-idle-reclaim.html

NEW QUESTION # 54
Which factors should you consider when determining the GPU memory requirements for inferencing an AI model?

A. The number of parameters in the model and the floating point precision
B. The memory available on the host and the number of parameters in the model
C. The floating point precision and the number of layers in the model
D. The memory available on the host and the number of convolutional layers in the model

Answer: A

Explanation:
GPU memory for inferencing is primarily driven by the model size, which depends on the number of parameters and the precision used to store them, such as FP32, FP16, BF16, or INT8.

NEW QUESTION # 55
A customer's security policy requires that all management traffic use only approved cryptographic algorithms and that firmware downgrades be blocked on their ProLiant servers.
What should you recommend?

A. Configure iLO directory integration with Kerberos authentication
B. Enable UEFI Secure Boot and set an administrator password
C. Set the iLO security state to High Security or higher and enable the server configuration lock
D. Disable the iLO dedicated network port and use the shared host port with VLAN tagging

Answer: C

Explanation:
iLO security states such as High Security, FIPS, and CNSA restrict management interfaces to approved cipher suites and enforce authentication for all interfaces. The server configuration lock detects and blocks unauthorized hardware and firmware changes, including downgrades, and can halt the server if tampering is detected.

NEW QUESTION # 56
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. Set up one Github repo, to which they will refer the Airflow framework, with the existing GitHub repos added as submodules.
B. Replicate the built-in Airflow framework to the namespace of each user with an existing GitHub repo.
C. Configure the Airflow framework to reference each existing GitHub repo using individual secrets for the credentials.
D. Create a recurring Spark pipeline to copy the DAGs from each GitHub to the Shared Data Volume from which Airflow pulls the DAGs.

Answer: A

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 # 57
A customer must encrypt OS volumes using BitLocker with hardware-backed key protection on new ProLiant Gen11 servers.
What must be present?

A. iLO Advanced Premium Security Edition
B. Self-encrypting drives with a local key manager
C. A TPM 2.0 module enabled in UEFI mode
D. A Smart Array controller with a Secure Encryption license

Answer: C

Explanation:
BitLocker uses TPM 2.0 to seal the volume encryption key to platform measurements. Controller- based Secure Encryption and SEDs protect data at rest at the drive level but do not provide the TPM key protector BitLocker requires.

NEW QUESTION # 58
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