Last Updated: Aug 17, 2026
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| Section | Objectives |
|---|---|
| Deployment and Operations | - Deployment models for AI solutions - Lifecycle management of AI infrastructure - Monitoring and optimization |
| Data Management and Governance | - Data governance and compliance - Data lifecycle management |
| HPE Private Cloud AI Fundamentals | - Overview of private cloud AI concepts - Core AI workload characteristics |
| HPE GreenLake for AI Solutions | - GreenLake architecture and services - Consumption-based IT model for AI |
| AI Infrastructure Design | - Compute and GPU considerations - Storage and data pipeline design - Networking for AI workloads |
| Security in Private Cloud AI | - Identity and access management - Workload and data protection |
1. A customer is expanding their HPE Private Cloud AI "Medium" configuration to support a new generative AI inferencing workload. They are concerned about network congestion and latency, as the AI workload is known to generate large, sudden bursts of traffic between the compute nodes and the storage system.
The solution uses NVIDIA Spectrum SN4700M switches for the AI interconnect.
Which feature of these switches is specifically designed to handle bursty traffic and prevent packet loss in a lossless Ethernet fabric?
A) A fully shared buffer architecture that can dynamically absorb traffic bursts from any port.
B) Support for Fibre Channel over Ethernet (FCoE) encapsulation.
C) The ability to route traffic based on application-layer metadata.
D) Integrated Silicon Root of Trust to validate the switch firmware integrity.
2. A customer wants to deploy a turnkey private cloud for a variety of generative AI workloads, including RAG-based chatbots and some model fine-tuning. One of their key IT stakeholders is the data engineer.
Which specific challenge for a data engineer is directly addressed by the HPE Data Fabric component within HPE Private Cloud AI?
A) The high cost of NVIDIA AI Enterprise software licenses.
B) The difficulty of managing GPU cluster utilization for training jobs.
C) The effort required to create and manage data pipelines and unify diverse, siloed data sources (files, objects, tables) into a single accessible platform.
D) The complexity of writing Python code in a Jupyter Notebook.
3. An architect has used the HPE Intelligent Configurator and determined that a "Medium - Standard" configuration is required.
When creating the final quote in One Config Advanced (OCA), what is the mandatory prerequisite that the customer is responsible for providing for the solution to be valid?
A) A list of all end-users who will access the system.
B) A subscription to the NVIDIA NGC catalog for downloading AI models.
C) A valid, bring-your-own-license (BYOL) for VMware vSphere Foundation (VVF) or VMware Cloud Foundation (VCF).
D) A rack elevation diagram for the data center.
4. After an architect selects the "HPE Private Cloud AI - Large - Expanded" Smart Template in OCA, they see it includes a significant number of "HPE Factory Express Complex Unit of SVC" services.
What is the purpose of these bundled services?
A) To provide a credit for future software purchases on the HPE GreenLake cloud.
B) To allow the customer to exchange the hardware for the next generation at no cost.
C) To cover the pre-integration and configuration of the entire solution at an HPE factory before shipment.
D) To provide on-site training for the customer's data science team.
5. An architect is presenting to a customer who is an 'Early AI User'. The customer is unsure about the differences between their two main project ideas.
Project 1: Create an internal chatbot that can answer HR policy questions by accessing the live employee handbook stored on a shared drive.
Project 2: Teach a general-purpose chatbot to adopt the company's formal communication style and tone for drafting official press releases.
How should the architect categorize the primary AI task for each project? (Choose 2.)
A) Project 1 is a RAG (Retrieval-Augmented Generation) workload.
B) Project 1 is a model training workload.
C) Project 2 is a classic inferencing workload with no model customization.
D) Project 2 is a fine-tuning workload.
Solutions:
| Question # 1 Answer: A | Question # 2 Answer: C | Question # 3 Answer: C | Question # 4 Answer: C | Question # 5 Answer: A,D |
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