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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Managing and Operating HPE Private Cloud AI Solutions | 30% | - Describe how to manage users and access control - Explain backup and recovery procedures - Identify troubleshooting procedures and common issues - Explain how to monitor HPE Private Cloud AI performance and health - Identify how to manage storage and data resources - Describe the tools and methods for managing HPE Private Cloud AI |
| Topic 2: Architecting HPE Private Cloud AI Solutions | 20% | - Explain common AI use cases and how they map to workloads - Identify components of the HPE Private Cloud AI architecture - Describe the AI/ML lifecycle and data pipeline requirements - Describe how HPE Private Cloud AI supports AI/ML workloads - Explain the HPE Private Cloud AI sizing and configuration guidelines |
| Topic 3: Installing and Configuring HPE Private Cloud AI Solutions | 30% | - Explain how to deploy and configure HPE Private Cloud AI components - Identify the steps to configure the HPE Private Cloud AI environment - Describe the prerequisites for installing HPE Private Cloud AI - Identify how to access and use HPE Private Cloud AI management interfaces - Describe how to validate the HPE Private Cloud AI installation |
| Topic 4: Supporting HPE Private Cloud AI Solutions | 20% | - Explain how to work with HPE support services - Describe support resources and documentation - Identify how to perform firmware and software updates - Describe capacity planning and optimization best practices |
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NEW QUESTION # 69
An architect is designing an AI solution to create a medical chatbot that assists doctors by answering questions based on the latest published medical research. The system must be highly reliable, and its answers must be traceable to the source publications. The customer has highlighted that their internal data science team lacks the expertise for complex model retraining but can manage data ingestion pipelines.
Given the customer requirements, which architectural components should the architect include in the solution design? (Select all that apply.)
```
Customer Requirements:
- AI Use Case: Medical Research Q&A Chatbot
- Key Constraint: Information must be current and verifiable.
- Team Skills: Limited AI model training expertise.
- Data Source: Continuously updated database of medical journals.
```
Answer: A,C,E,F
NEW QUESTION # 70
A customer's ML Engineer states, "We need to deploy our trained models, and our top priority is simplifying the process. We want to treat our models like cattle, not pets-packaging them into standardized, optimized containers that we can deploy and scale easily via an API." This statement describes the primary benefit of which software component in the HPE Private Cloud AI stack?
Answer: B
NEW QUESTION # 71
An enterprise architecture team is debating the best method to adapt a general-purpose Large Language Model (LLM) for two different, highly-specialized internal use cases:
1. Use Case A: A customer support chatbot that must provide answers strictly based on a rapidly changing knowledge base of product manuals and technical notes. Verifiability and traceability of the information source are critical.
2. Use Case B: An internal code generation assistant that needs to learn the company's specific coding style, proprietary frameworks, and API usage patterns from a large, static codebase.
Which are the most appropriate strategies for these use cases? (Choose 2.)
Answer: A,D
NEW QUESTION # 72
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,C,F
NEW QUESTION # 73
A customer is using the NVIDIA NeMo framework within HPE Private Cloud AI to build a custom generative AI application. They need to fine-tune a foundation model using a proprietary dataset. They also want to ensure the final application does not produce toxic content or veer into off-topic conversations.
Which specific toolkits within the NeMo framework should they use to achieve these two distinct goals?
(Choose 2.)
Answer: A,C
NEW QUESTION # 74
......
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