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| Section | Weight | Objectives |
|---|---|---|
| Managing and Operating HPE Private Cloud AI Solutions | 30% | - Identify how to manage storage and data resources - Describe how to manage users and access control - Explain how to monitor HPE Private Cloud AI performance and health - Identify troubleshooting procedures and common issues - Explain backup and recovery procedures - Describe the tools and methods for managing HPE Private Cloud AI |
| Installing and Configuring HPE Private Cloud AI Solutions | 30% | - Describe the prerequisites for installing HPE Private Cloud AI - Describe how to validate the HPE Private Cloud AI installation - Identify how to access and use HPE Private Cloud AI management interfaces - Identify the steps to configure the HPE Private Cloud AI environment - Explain how to deploy and configure HPE Private Cloud AI components |
| Architecting HPE Private Cloud AI Solutions | 20% | - Explain the HPE Private Cloud AI sizing and configuration guidelines - Identify components of the HPE Private Cloud AI architecture - Explain common AI use cases and how they map to workloads - Describe how HPE Private Cloud AI supports AI/ML workloads - Describe the AI/ML lifecycle and data pipeline requirements |
| Supporting HPE Private Cloud AI Solutions | 20% | - Identify how to perform firmware and software updates - Explain how to work with HPE support services - Describe support resources and documentation - Describe capacity planning and optimization best practices |
1. An enterprise wants to build an internal AI platform that functions like an "App Store." Their goal is to provide various internal development teams with a catalog of curated AI models that they can easily deploy and integrate into their applications without needing deep AI expertise. The platform must be secure, reliable, and scalable.
Which software components within the HPE Private Cloud AI stack are essential to building this platform? (Select all that apply.)
A) NVIDIAAI Enterprise for enterprise-grade support and security
B) NVIDIA NIM (NVIDIA Inference Microservices)
C) The HPE Intelligent Configurator
D) HPE AI Essentials for a unified data science and MLOps plane
E) A collection of pre-trained community and custom-built AI models
2. A customer is considering the HPE Private Cloud AI solution. They need to run a moderately sized RAG application for 150 users. They do not have any fine-tuning requirements.
Why would an architect recommend a "Medium" configuration over a "Large" configuration for this customer?
A) The Medium configuration has more storage capacity than the Large configuration.
B) The Medium configuration is the only one that includes the HPE AI Essentials software.
C) The L40S GPUs in the Medium configuration are more cost-effective and power-efficient for this specific inference-heavy workload.
D) The Large configuration does not support Retrieval-Augmented Generation (RAG).
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 rack elevation diagram for the data center.
D) A valid, bring-your-own-license (BYOL) for VMware vSphere Foundation (VVF) or VMware Cloud Foundation (VCF).
4. A customer is building a large-scale AI training cluster using HPE Private Cloud AI.
They have two main requirements for the storage solution:
1. It must provide extremely high throughput and low latency to feed data to a large cluster of NVIDIA H100 GPUs.
2. It must be able to scale performance and capacity independently to accommodate future growth without costly overprovisioning.
The architect is proposing HPE GreenLake for File Storage.
Which architectural features of this solution directly address the customer's requirements? (Select all that apply.)
```
Customer Requirements:
1. High performance for AI training workloads.
2. Independent scaling of performance and capacity.
```
A) It relies on traditional hard disk drives (HDDs) for cost-effective capacity, with a small flash tier for caching.
B) It supports GPUDirect Storage (GDS) over a high-speed NVMe-oF (RoCE) network, enabling a direct, low-latency path between storage and GPUs.
C) It is managed exclusively through a command-line interface (CLI) for maximum control.
D) It uses a disaggregated architecture, allowing controller nodes (performance) and storage shelves (capacity) to be scaled separately.
E) It includes HPE Data Fabric, providing a global namespace across the worker nodes' local drives.
5. A financial services company, currently at the 'AI Pro' maturity level, wants to build a private cloud solution to support two primary initiatives:
1. Initiative 1: Fine-tune a proprietary 70B parameter LLM for fraud detection, requiring maximum training performance.
2. Initiative 2: Deploy a customer-facing RAG-based chatbot for 500 concurrent users.
The customer wants a single, integrated solution that can handle both workloads efficiently. They have a new data center with ample power and cooling.
Which HPE Private Cloud AI configuration should the architect recommend?
A) Two separate solutions: a Small - Expanded for the RAG workload and a Large - Standard for the fine-tuning workload.
B) Large - Expanded, because its NVIDIA H100 NVL GPUs and eight worker nodes are required for the intensive fine-tuning workload.
C) A custom-built solution using HPE ProLiant DL325 servers and NVIDIA L4 GPUs.
D) Medium - Expanded, because it supports a high number of concurrent users for RAG.
Solutions:
| Question # 1 Answer: A,B,D,E | Question # 2 Answer: C | Question # 3 Answer: D | Question # 4 Answer: B,D | Question # 5 Answer: B |
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