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| Section | Objectives |
|---|---|
| Topic 1: HPE Private Cloud AI Fundamentals | - Overview of private cloud AI concepts - Core AI workload characteristics |
| Topic 2: Deployment and Operations | - Lifecycle management of AI infrastructure - Monitoring and optimization - Deployment models for AI solutions |
| Topic 3: Security in Private Cloud AI | - Identity and access management - Workload and data protection |
| Topic 4: AI Infrastructure Design | - Networking for AI workloads - Compute and GPU considerations - Storage and data pipeline design |
| Topic 5: HPE GreenLake for AI Solutions | - GreenLake architecture and services - Consumption-based IT model for AI |
| Topic 6: Data Management and Governance | - Data governance and compliance - Data lifecycle management |
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NEW QUESTION # 59
A customer who is an "AI Beginner" wants to start an AI inferencing project at their edge locations.
Their goal is to analyze security camera feeds to help prevent theft. They have a limited budget and IT staff at the edge sites.
Which HPE AI solution is the most appropriate to position for this specific scenario?
Answer: E
NEW QUESTION # 60
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.
```
Answer: D,E
NEW QUESTION # 61
A global logistics company is designing an enterprise-grade AI solution. The project has two main goals:
1. Goal 1: Develop a highly accurate, proprietary logistics optimization model by fine-tuning a foundation model on the company's massive, confidential shipping dataset (20TB). This requires a secure, high- performance, multi-node training environment.
2. Goal 2: Deploy a generative AI-powered chatbot for the customer service department. The chatbot must provide real-time shipment status and answer policy questions based on a knowledge base that is updated hourly.
The company is classified as an 'AI Pro,' with a formal AI strategy and a Center of Excellence, but they want a turnkey solution to accelerate time-to-market.
Which components and strategies should the architect propose to meet all the customer's requirements?
(Select all that apply.)
```
Customer Profile:
- Industry: Global Logistics
- AI Maturity: AI Pro
- Key Workloads: Large-scale Fine-Tuning, Real-time RAG
- Desired Solution: Turnkey, enterprise-grade private cloud
```
Answer: A,B,E
NEW QUESTION # 62
An architect is sizing an HPE Private Cloud AI solution. The customer plans to deploy a generative AI application for 150 concurrent users that requires Retrieval-Augmented Generation (RAG).
The architect enters the following into the HPE Intelligent Configurator:
```
- Use case: Text Generation
- Number of users: 100-250
- RAG: Yes
- Model: Llama 2 13B (tool default for this use case)
```
Based on the provided inputs, which HPE Private Cloud AI configuration will the HPE Intelligent Configurator most likely recommend?
Answer: B
NEW QUESTION # 63
A customer explains that their data engineers are spending too much time managing disparate data pipelines with a complex set of open-source tools. They are an 'Early AI User' trying to standardize their approach.
Which value proposition of HPE Private Cloud AI directly addresses this specific stakeholder's pain point?
Answer: A
NEW QUESTION # 64
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