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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Solution Design for Enterprise Workloads | 30% | - Scalability, availability and performance optimization - Design for AI, HPC and mission-critical workloads - Requirements analysis and environment assessment |
| Topic 2: Customer Success and Solution Optimization | 10% | - Solution demonstration and value justification - Troubleshooting and continuous improvement |
| Topic 3: Implementation and Lifecycle Management | 15% | - Deployment planning and validation - Upgrade, maintenance and monitoring |
| Topic 4: HPE Compute Architecture and Technologies | 25% | - HPE ProLiant, Apollo and Superdome Flex platforms - Virtualization and container integration - Compute component selection: processors, memory, storage, accelerators |
| Topic 5: Integration with Data Center Infrastructure | 20% | - Security, compliance and data protection - Networking and storage integration - HPE OneView management and automation |
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NEW QUESTION # 15
You are recommending HPE ProLiant DL servers and HPE Alletra Storage MP B10000 for a customer's Red Hat OpenShift platform.
What should you recommend to simplify storage provisioning for workloads running on the platform?
Answer: A
Explanation:
The HPE CSI Driver/Operator for Kubernetes enables dynamic provisioning and management of persistent storage for Kubernetes/OpenShift workloads on HPE Alletra Storage MP B10000.
vVols are for VMware environments, not OpenShift workload storage provisioning.
NEW QUESTION # 16
A customer is evaluating HPE Private Cloud AI for an AI PoC and must choose between a small configuration and a Developer System configuration.
What is one difference that you should explain?
Answer: D
Explanation:
HPE Private Cloud AI offers several infrastructure configurations designed to accommodate different AI development, testing, and production requirements.
The Developer System is an entry-level, on-premises configuration intended for application prototyping, proof-of-concept development, and small-scale AI workloads.
One significant architectural difference is its storage design. The Developer System incorporates integrated storage, whereas the standard Small configuration uses dedicated HPE GreenLake for File Storage infrastructure.
This difference reduces the physical infrastructure footprint and deployment complexity of the Developer System, making it appropriate for organizations beginning their AI adoption journey.
HPE's original Developer System configuration included 32 TB of integrated storage and two NVIDIA H100 NVL GPUs. Newer configurations have revised GPU and storage specifications, including integrated NFS and MinIO storage.
Importantly, the Developer System runs the same HPE and NVIDIA software platform as the larger configurations, although its hardware capacity imposes practical workload limitations.
Option A is incorrect because the Developer System operates on customer-premises hardware. Option B is inaccurate because the documented configurations do not establish L4 GPUs as the defining difference.
Option D incorrectly suggests that the Developer System excludes major software capabilities.
The fundamental difference identified by the question is the use of integrated storage rather than dedicated GreenLake for File Storage.
Official HPE Reference: HPE Private Cloud AI Administration Guide - Developer System; HPE Private Cloud AI QuickSpecs - Configuration Information; HPE Private Cloud AI - Expansion Rack and Configuration Comparison.
HPE Support
NEW QUESTION # 17
A customer intends to implement machine learning models but has limited visibility into where business data is produced and how it is managed across departments.
During the readiness assessment, which step would best support an accurate evaluation of their AI deployment potential?
Answer: A
Explanation:
An effective AI readiness assessment requires a comprehensive understanding of the organization's data landscape before selecting machine learning tools, infrastructure, or deployment architectures.
The architect should begin by identifying enterprise data sources, their formats, ownership responsibilities, storage locations, access mechanisms, and movement patterns between departments.
This process establishes whether the organization possesses accessible, relevant, sufficiently reliable data to support its intended machine learning applications.
Understanding data ownership is particularly important because it identifies the stakeholders responsible for authorizing access, maintaining quality, enforcing governance policies, and determining permitted uses.
Data-flow analysis also reveals dependencies between transactional systems, warehouses, analytical platforms, and downstream applications. These dependencies affect data ingestion architecture, network capacity, workload placement, security controls, and operational requirements.
HPE's Artificial Intelligence Transformation Workshop explicitly includes identifying data sources, dependencies, and organizational readiness as fundamental planning activities.
Option A prematurely introduces technical integration before identifying the actual data landscape. Option B addresses business continuity but does not adequately assess AI data readiness. Option C bypasses essential discovery and may produce an unrepresentative pilot.
Option D establishes the information needed to assess feasibility, identify risks, and prepare accurate customer requirements for the proposed AI solution.
Official HPE Reference: HPE Artificial Intelligence Transformation Workshop - Data Sources, Dependencies, and AI Roadmap; HPE AI Services - Data Assessment and AI Strategy.
HPE EUROPE
NEW QUESTION # 18
A customer asks about the options HPE Private Cloud AI includes out of the box for hyperparameter optimization (HPO) during fine-tuning.
What is one thing that you should explain?
Answer: A
Explanation:
HPE AI Essentials includes Kubeflow, and Kubeflow includes Katib for hyperparameter tuning experiments. The other options incorrectly map HPO to MLflow, Ray Tune exposure, or Spark/Livy.
Reference:
https://support.hpe.com/hpesc/public/docDisplay?docId=a00ecp55hen_us&docLocale=en_US&p age=reference%2Fkubernetes%2Fusing-kubernetes%2Fgeneral-functionality%2Ftutorials%2Fkubeflow%2FTutorial_Katib_Hyperparameter_Tuning.html
https://www.hpe.com/us/en/collaterals/collateral.a50014263enw.html
NEW QUESTION # 19
A customer wants you to design a solution for effectively near 100% liquid cooling for their HPE Cray XD systems.
Which solution should you propose?
Answer: A
Explanation:
HPE Cray XD DLC removes the main component heat, while HPE RDHX captures the remaining hot exhaust air and transfers it to facility water, enabling effectively near-100% heat removal to liquid cooling.
Reference: https://www.hpe.com/us/en/collaterals/collateral.a50004292enw.html
NEW QUESTION # 20
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