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| Section | Weight | Objectives |
|---|---|---|
| Integration with Data Center Infrastructure | 20% | - Networking and storage integration - HPE OneView management and automation - Security, compliance and data protection |
| Customer Success and Solution Optimization | 10% | - Solution demonstration and value justification - Troubleshooting and continuous improvement |
| Implementation and Lifecycle Management | 15% | - Upgrade, maintenance and monitoring - Deployment planning and validation |
| 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% | - Requirements analysis and environment assessment - Design for AI, HPC and mission-critical workloads - Scalability, availability and performance optimization |
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NEW QUESTION # 16
Which HPE Private Cloud AI configuration is designed for early development and prototyping use cases?
Answer: B
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 # 17
A customer runs latency-sensitive network functions in virtual machines and reports that the hypervisor virtual switch adds unacceptable overhead.
What should you recommend?
Answer: C
Explanation:
SR-IOV allows a physical adapter to present virtual functions that are passed directly to guest VMs, bypassing the hypervisor networking stack and reducing latency and CPU overhead. Note that SR-IOV can restrict features such as live migration in some configurations.
NEW QUESTION # 18
An organization is planning to run AI inferencing for customer service. The organization has strict environmental efficiency standards. They have facility water access at the rack.
Which server configuration should you recommend?
Answer: C
Explanation:
The DL380a Gen12 is accelerator-optimized for AI inferencing, and 70% direct liquid cooling is the best fit when the customer has facility water at the rack and strict efficiency requirements. Air cooling, rear-door cooling, or closed-loop cooling would be less efficient for this scenario.
Reference: https://www.hpe.com/emea_europe/en/compute/hpe-proliant-compute/dl380a- gen12.html
NEW QUESTION # 19
A customer wants on-premises HPE compute but needs to avoid a large upfront capital purchase and pay based on actual consumption, with buffer capacity installed ahead of demand.
What should you recommend?
Answer: C
Explanation:
HPE GreenLake delivers on-premises infrastructure as a consumption-based service. Buffer capacity is installed and available immediately but is billed only when used, which combines cloud-like economics with on-premises control.
NEW QUESTION # 20
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?
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 # 21
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