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HP HPE7-S01 Exam Syllabus Topics:

Section	Objectives
Security and Lifecycle Management	- Secure compute architecture principles - Firmware, patching, and lifecycle management
HPE Compute Architecture Design	- Workload analysis and requirements mapping - Compute infrastructure planning and sizing
HPE Compute Solutions Portfolio	- HPE ProLiant server solutions - HPE GreenLake compute offerings - HPE Synergy and composable infrastructure
Solution Design and Integration	- Storage and networking integration considerations - High availability and scalability design
Operations and Troubleshooting	- Monitoring and performance optimization - Incident resolution and capacity planning

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HP Advanced HPE Compute Architect Solutions Written Exam Sample Questions (Q11-Q16):

NEW QUESTION # 11
Which factors should you consider when determining the GPU memory requirements for inferencing an AI model?

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

Answer: D

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 # 12
An automotive engineering team is running inference workloads for traffic simulation and computer vision models using KServe on HPE Private Cloud AI.
The team also uses GPU-enabled notebooks running on HPE Private Cloud AI to experiment with new models. They want to automatically adjust GPU usage for the different models based on current demand, as well as ensure that the notebooks never prevent the inferencing workloads from receiving the GPUs they need.
Which two features support this functionality? (Choose two.)

A. Canary roll outs for KServe
B. Kyverno mutating admission policies
C. Autoscaling for KServe
D. HPE AI Essentials scheduler priorities

Answer: C,D

Explanation:
KServe autoscaling adjusts inference deployment capacity based on demand. HPE AI Essentials scheduler priorities ensure higher-priority inference workloads can get GPUs ahead of lower- priority notebook workloads.
Reference: https://kserve.github.io/website/docs/model-serving/predictive- inference/autoscaling/hpa-autoscaler
https://support.hpe.com/hpesc/public/docDisplay?docId=a00aie17hen_us&docLocale=en_US&pa ge=pcai%2FGenAI%2Fdeploying-knowledge-base.html

NEW QUESTION # 13
You are demonstrating HPE Private Cloud AI and HPE Machine Learning Inference Software to a customer. The customer says that the organization has a custom model, stored in an OpenLLM hub, which they would like to deploy with HPE Machine Learning Inference Software.
What should you explain?

A. HPE Machine Learning Inference Software supports pulling this model from the hub and deploying it.
B. HPE Machine Learning Inference Software can deploy this model if the customer packages it with Bento Archive and saves it to a PVC in HPE Private Cloud AI.
C. While HPE Machine Learning Inference Software does not support deploying custom models, Ray Serve, also provided by HPE Private Cloud AI, does.
D. While HPE Machine Learning Inference Software does not support deploying custom models, KServe, also provided by HPE Private Cloud AI, does.

Answer: A

Explanation:
MLIS supports OpenLLM registries, which allow it to pull OpenLLM-compatible models directly from the Hugging Face/OpenLLM hub and use them to create deployments.
Reference:
https://support.hpe.com/hpesc/public/docDisplay?docId=a00aie112hen_us&docLocale=en_US&p age=MLIS%2Fregistries%2Fopenllm.html

NEW QUESTION # 14
A customer runs a transactional database with a write-intensive I/O profile on twelve SAS SSDs attached to an HPE MR controller and requires fault tolerance with minimal write penalty.
Which RAID level should you recommend?

A. RAID 50
B. RAID 5
C. RAID 6
D. RAID 10

Answer: D

Explanation:
RAID 10 mirrors and stripes, incurring a write penalty of 2 compared with 4 for RAID 5 and 6 for RAID 6. For write-heavy transactional workloads where usable capacity is secondary to write performance, RAID 10 is the appropriate choice.

NEW QUESTION # 15
You are helping a customer plan to use their HPE Private Cloud AI solution securely.
What is one recommendation to make?

A. Replace the certificate on the Istio ingress gateway with a CA-signed one.
B. Configure the SPIRE server to rotate trust bundles on a weekly basis.
C. Add Kyverno policies to individual user namespaces to prevent access to other namespaces.
D. Add a custom realm to the default Keycloak installation.

Answer: A

Explanation:
HPE AI Essentials includes built-in self-signed certificates, and HPE documents replacing them with custom/CA-signed certificates for secure production use. The other options are not standard recommended security steps for HPE Private Cloud AI.
Reference:
https://support.hpe.com/hpesc/public/docDisplay?docId=a00aie18hen_us&docLocale=en_US&pa ge=Security%2FCA-SSL-Cert%2Fupdate-cert.html
https://support.hpe.com/hpesc/public/docDisplay?docId=a00aie19hen_us&docLocale=en_US&pa ge=Security%2FCA-SSL-Cert%2Fusing-cert-manager.html

NEW QUESTION # 16
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