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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Integration with Data Center Infrastructure | 20% | - Security, compliance and data protection - HPE OneView management and automation - Networking and storage integration |
| Topic 2: HPE Compute Architecture and Technologies | 25% | - HPE ProLiant, Apollo and Superdome Flex platforms - Compute component selection: processors, memory, storage, accelerators - Virtualization and container integration |
| Topic 3: Customer Success and Solution Optimization | 10% | - Troubleshooting and continuous improvement - Solution demonstration and value justification |
| Topic 4: Implementation and Lifecycle Management | 15% | - Upgrade, maintenance and monitoring - Deployment planning and validation |
| Topic 5: Solution Design for Enterprise Workloads | 30% | - Scalability, availability and performance optimization - Requirements analysis and environment assessment - Design for AI, HPC and mission-critical workloads |
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NEW QUESTION # 62
Which HPE Private Cloud AI configuration is designed for early development and prototyping use cases?
Answer: C
Explanation:
The HPE Private Cloud AI Developer System is the entry configuration intended for early development, experimentation, and prototyping before moving to small, medium, or large production-oriented configurations.
NEW QUESTION # 63
A potential HPE Private Cloud AI customer has data stored in several locations. These include a Cassandra database, a Hive data warehouse, and a data lakehouse on S3 object storage.
Which correctly describes the HPE Private Cloud AI's ability to query data across these sources?
Answer: D
Explanation:
HPE AI Essentials uses EzPresto for federated queries across multiple data sources, and it supports querying configured sources such as Cassandra, Hive, and S3/lakehouse data sources through the Data Lakehouse Gateway.
Reference:
https://support.hpe.com/hpesc/public/docDisplay?docId=a00aie111hen_us&docLocale=en_US&p age=EzPresto%2Fezpresto.html
NEW QUESTION # 64
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?
Answer: D
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 # 65
You are recommending HPE Slingshot as the interconnect for an HPC cluster that also runs AI training.
Which statement correctly describes this solution's benefits for this use case?
Answer: C
Explanation:
Distributed AI training is sensitive to tail latency during GPU synchronization. HPE Slingshot is designed for HPC/AI fabrics with adaptive routing and congestion control, helping reduce latency spikes and maintain performance at scale.
NEW QUESTION # 66
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: A
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 # 67
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