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HP HPE7-S01 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Demonstrate AI Solutions	10%	- AI solution demonstration 1. Solution positioning 2. Business value presentation 3. Customer use cases
Topic 2: Explain HPE Compute AI and HPC Solution Components and Architecture	38%	- AI and HPC architectures 1. GPU-accelerated computing 2. High-performance workload platforms 3. NVIDIA integration - Management and automation 1. Lifecycle management 2. HPE GreenLake 3. HPE OneView - HPE Compute portfolio 1. HPE ProLiant servers 2. HPE Synergy 3. HPE Cray systems
Topic 3: Manage Implementation and Startup of HPE AI and HPC Solutions	2%	- Deployment and validation 1. Operational readiness 2. Implementation planning 3. Configuration verification
Topic 4: Design HPE Solutions for AI and HPC Workloads	50%	- Solution architecture and design 1. Performance optimization 2. Workload analysis 3. Capacity planning - Infrastructure integration 1. Hybrid cloud architectures 2. Scalability and resiliency design 3. Compute, storage, and networking integration

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

NEW QUESTION # 53
A customer has developed a custom NIM which they store in their NGC Private Registry. The customer would like to use that NIM in inferencing workloads running on HPE Private Cloud AI.
What should you recommend?

A. Using MLIS with an NGC registry that points to the customer's private registry
B. Using Ray Serve with custom-built deployments based on the custom NIM image
C. Finding and importing NVIDIA AI blueprint that supports the custom NIM
D. Downloading the custom NIM to individual users' models-pvc and manually creating KServe endpoints to deploy the NIM

Answer: A

Explanation:
HPE Private Cloud AI uses HPE MLIS to deploy NIM-based inference services, and MLIS supports configuring an NGC registry so it can pull NIMs from NVIDIA NGC/private registry sources. Manual KServe or Ray Serve deployment is unnecessary for this use case.
Reference:
https://support.hpe.com/hpesc/public/docDisplay?docId=a00aie111dhen_us&docLocale=en_US& page=pcai%2FCatalogs%2Fdeploy-NIM-using-s3.html

NEW QUESTION # 54
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?

A. The solution's default Kuberay cluster does not support GPU workers, but the customer can deploy an additional Kuberay cluster that does.
B. The solution's Kuberay cluster supports GPUs by default and can autoscale up to the maximum number of GPUs in the solution.
C. The solution's Kuberay cluster supports GPUs by default and can autoscale up to the maximum number of GPUs on a single AI-optimized node.
D. The solution's Kuberay cluster has a GPU worker, and AI administrators can define the number of GPUs for that worker.

Answer: D

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 # 55
A customer wants to use the Spark framework included in HPE Private Cloud AI to ingress and process data stored in an external MinIO object store. The customer wants to avoid distributing the store's access and secret keys to AI Users.
What should you advise AI administrators to do?

A. Integrate the MinIO object store with the SSO and authorization framework provided by HPE Private Cloud AI's Keycloak.
B. Create a custom spark image that is pre-integrated with the MinIO object store.
C. Set up an HPE AI Essentials data source to the MinIO object store.
D. Replicate the data from the MinIO object store to a NAS store compatible with the HPE AI Essentials Data Volumes format.

Answer: C

Explanation:
HPE AI Essentials lets administrators connect external S3 object stores such as MinIO as data sources. Users can then access them from clients such as Spark without being given the access key or secret key.
Reference:
https://support.hpe.com/hpesc/public/docDisplay?docId=a00aie18hen_us&docLocale=en_US&pa ge=ManageClusters%2Fconnect-object-stores.html

NEW QUESTION # 56
What is one benefit of the NVIDIA AI Enterprise license provided in HPE Private Cloud AI?

A. NVIDIA Data Center GPU Manager (DCGM) available from HPE Private Cloud AI platform
B. Drivers to permit executing training pipelines on GPUs outside of containers
C. Autoscaling of models across one or more GPUs based on current demand
D. Entitlement to use NVIDIA components in blueprints for accelerating AI application development

Answer: D

Explanation:
The NVIDIA AI Enterprise license in HPE Private Cloud AI provides entitlement to NVIDIA enterprise AI software components used in AI workflows/blueprints, helping customers build and deploy AI applications faster.
Reference: https://docs.nvidia.com/ai-enterprise/software/latest/index.html
https://www.nvidia.com/en-us/data-center/products/ai-enterprise/

NEW QUESTION # 57
An HPE Private Cloud AI customer wants to archive Prometheus metrics collected on the solution.
What should you recommend?

A. Set up an external OpenTelemetry (OTel) endpoint and register it in HPE AI Essentials.
B. Change the PV in the Prometheus namespace to use Retain mode.
C. Configure the Prometheus framework and enlarge the kubeprom-operator volume to at least
100G.
D. Install the Grafana framework on HPE Private Cloud AI and set up Prometheus as a data source.

Answer: A

Explanation:
HPE AI Essentials includes OpenTelemetry for forwarding metrics and logs to external monitoring systems, which is the appropriate approach for archiving Prometheus metrics outside the platform. Enlarging Prometheus storage or changing PV retention is not the recommended archival method.
Reference:
https://support.hpe.com/hpesc/public/docDisplay?docId=a00aie111dhen_us&docLocale=en_US& page=Observability%2Fotel.html

NEW QUESTION # 58
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