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| Section | Weight | Objectives |
|---|---|---|
| Integration with Data Center Infrastructure | 20% | - Networking and storage integration - Security, compliance and data protection - HPE OneView management and automation |
| Implementation and Lifecycle Management | 15% | - Deployment planning and validation - Upgrade, maintenance and monitoring |
| Customer Success and Solution Optimization | 10% | - Solution demonstration and value justification - Troubleshooting and continuous improvement |
| HPE Compute Architecture and Technologies | 25% | - Virtualization and container integration - HPE ProLiant, Apollo and Superdome Flex platforms - Compute component selection: processors, memory, storage, accelerators |
| Solution Design for Enterprise Workloads | 30% | - Design for AI, HPC and mission-critical workloads - Scalability, availability and performance optimization - Requirements analysis and environment assessment |
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NEW QUESTION # 62
What is one benefit of the HPE curated images for Spark provided in HPE AI Essentials?
Answer: C
Explanation:
HPE-curated Spark images are designed to work with HPE AI Essentials data sources; for these images, users can access configured sources without manually distributing secrets, unlike generic Spark OSS images.
Reference:
https://support.hpe.com/hpesc/public/docDisplay?docId=a00aie111dhen_us&docLocale=en_US& page=Spark%2Fcreating_spark_application.html
NEW QUESTION # 63
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.)
Answer: A,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 # 64
You are meeting with the AI project team at Company PXXX for the first time. They have invited IT staff, sales leaders, and back-office representatives.
What is the best way to begin evaluating their environment?
Answer: C
Explanation:
For an AI project, the first evaluation should identify business goals, stakeholders, data sources, workflows, and dependencies across teams. GPU sizing and HPE configurations come later after requirements are understood.
NEW QUESTION # 65
Which statement correctly describes how you select HPE servers to act as nodes in a VMware vSAN ESA cluster?
Answer: B
Explanation:
For vSAN ESA, HPE directs customers to use certified vSAN ESA ReadyNode configurations listed in the Broadcom Compatibility Guide; BYO/custom configurations are associated with vSAN OSA, not ESA.
Reference: https://www.hpe.com/us/en/collaterals/collateral.a00067741enw.html
NEW QUESTION # 66
You are leading a workshop to help a customer deploy a pilot AI application.
With which data-related task should you help the customer?
Answer: C
Explanation:
AI pilot planning should start by mapping data locations, dependencies, access patterns, and latency/compliance constraints, then placing AI workloads close to the relevant data. Data movement, conversion, or insecure centralization are not appropriate first steps.
NEW QUESTION # 67
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