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| Section | Weight | Objectives |
|---|
| Topic 1: Architecting HPE Private Cloud AI Solutions | 20% | - Explain the HPE Private Cloud AI sizing and configuration guidelines - Explain common AI use cases and how they map to workloads - Identify components of the HPE Private Cloud AI architecture - Describe how HPE Private Cloud AI supports AI/ML workloads - Describe the AI/ML lifecycle and data pipeline requirements
|
| Topic 2: Installing and Configuring HPE Private Cloud AI Solutions | 30% | - Describe how to validate the HPE Private Cloud AI installation - Identify how to access and use HPE Private Cloud AI management interfaces - Describe the prerequisites for installing HPE Private Cloud AI - Explain how to deploy and configure HPE Private Cloud AI components - Identify the steps to configure the HPE Private Cloud AI environment
|
| Topic 3: Managing and Operating HPE Private Cloud AI Solutions | 30% | - Describe how to manage users and access control - Explain backup and recovery procedures - Identify how to manage storage and data resources - Explain how to monitor HPE Private Cloud AI performance and health - Describe the tools and methods for managing HPE Private Cloud AI - Identify troubleshooting procedures and common issues
|
| Topic 4: Supporting HPE Private Cloud AI Solutions | 20% | - Identify how to perform firmware and software updates - Explain how to work with HPE support services - Describe capacity planning and optimization best practices - Describe support resources and documentation
|
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HPE Private Cloud AI Solutions Sample Questions (Q57-Q62):
NEW QUESTION # 57
A customer is planning to deploy an HPE Private Cloud AI solution. Their IT department has provided a worksheet detailing the workload requirements. The solution architect needs to select the appropriate configuration size.
Review the customer's requirements:
```
- Primary Workload: Fine-tuning a 70-billion parameter foundation model
- Secondary Workload: AI Inferencing with RAG
- User Base: 50 developers during tuning; 300 concurrent users for inference
- Data Sensitivity: High (must remain on-premises)
- Power/Cooling: Standard data center air cooling available
```
Which HPE Private Cloud AI configuration is required to meet these demands, and what is the key component that justifies this choice? (Choose 2.)
- A. The key component is the NVIDIA H100 NVL GPU, designed for large-scale training and fine-tuning.
- B. The solution requires a Large configuration.
- C. The key component is the HPE ProLiant DL325 server in the management plane.
- D. The solution requires a Medium configuration.
- E. The key component is the NVIDIA L40S GPU, which is optimized for RAG workloads.
Answer: A,B
NEW QUESTION # 58
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 rack elevation diagram for the data center.
- B. A list of all end-users who will access the system.
- C. A valid, bring-your-own-license (BYOL) for VMware vSphere Foundation (VVF) or VMware Cloud Foundation (VCF).
- D. A subscription to the NVIDIA NGC catalog for downloading AI models.
Answer: C
NEW QUESTION # 59
A customer wants to enhance their existing Large Language Model (LLM) to provide more accurate and contextually relevant answers based on a proprietary, rapidly changing knowledge base of legal documents. They are considering two approaches: fine-tuning and Retrieval-Augmented Generation (RAG).
Review the data flow diagram for the proposed RAG implementation:
```
User Query -> [Query Encoder] -> Vector DB Search -> [Retrieved Documents] --+
|
+-> [LLM Prompt] -> LLM -> Response
```
Based on the diagram and the scenario, which statement accurately identifies a primary advantage of the RAG approach for this customer?
- A. RAG requires retraining the LLM whenever a new legal document is added to the knowledge base.
- B. RAG reduces the need for a vector database by directly integrating documents into the model.
- C. RAG allows the LLM to access the most current legal documents at inference time without daily retraining.
- D. RAG permanently modifies the LLM's internal weights to specialize in legal terminology.
Answer: C
NEW QUESTION # 60
A customer needs a solution for their deployed customer service chatbot. They state: "We don't need to change the model itself, but we need the chatbot to answer questions using our product documentation, which is updated every night. The answers must be fast and based on the latest documents." How would you categorize this workload?
- A. A RAG (Retrieval-Augmented Generation) inferencing workload.
- B. A large-scale model training workload.
- C. A model development and experimentation workload.
- D. A classic AI inferencing workload.
Answer: A
NEW QUESTION # 61
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 supports GPUDirect Storage (GDS) over a high-speed NVMe-oF (RoCE) network, enabling a direct, low-latency path between storage and GPUs.
- B. It relies on traditional hard disk drives (HDDs) for cost-effective capacity, with a small flash tier for caching.
- C. It includes HPE Data Fabric, providing a global namespace across the worker nodes' local drives.
- D. It is managed exclusively through a command-line interface (CLI) for maximum control.
- E. It uses a disaggregated architecture, allowing controller nodes (performance) and storage shelves (capacity) to be scaled separately.
Answer: A,E
NEW QUESTION # 62
......
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