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 effort required to create and manage data pipelines and unify diverse, siloed data sources (files, objects, tables) into a single accessible platform.
• C. The difficulty of managing GPU cluster utilization for training jobs.
• D. The complexity of writing Python code in a Jupyter Notebook.

You are positioning HPE Private Cloud AI to a customer who is an "AI Pro" and wants to scale their generative AI efforts.
Which key capabilities of the solution would you emphasize to this customer? (Select all that apply.)

• A. A simple, web-based interface for beginners to learn about AI concepts.
• B. The inclusion of a full-stack, enterprise-supported software suite including NVIDIA AI Enterprise and HPE AI Essentials.
• C. The option to choose a Large configuration with powerful NVIDIA H100 GPUs and a high-speed RoCE fabric for demanding fine-tuning workloads.
• D. Its ability to run on a single, low-power server for maximum efficiency.
• E. Its turnkey nature, which simplifies deployment and allows their expert team to focus on building models rather than integrating infrastructure.

An architect is designing an AI solution for a financial services company that needs to build a chatbot.
The chatbot must answer customer queries using the company's latest internal policy documents, which are updated daily. The company has a limited budget and no data scientists available for a lengthy model retraining project.
Which approach should the architect recommend?

• A. Use a pre-trained computer vision model to extract text from the policy documents.
• B. Train a new LLM from scratch using only the company's policy documents to ensure data privacy.
• C. Use a pre-trained model and implement a Retrieval-Augmented Generation (RAG) framework.
• D. Fine-tune a pre-trained model daily by retraining it on the entire set of policy documents.

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.
```

• A. A vector database to store embeddings of the medical research papers.
• B. A Retrieval-Augmented Generation (RAG) framework to query the vector database.
• C. A pre-trained, general-purpose Large Language Model (LLM).
• D. An edge-optimized server for model deployment in individual hospital rooms.
• E. A data ingestion and embedding pipeline to process and add new research to the vector database.
• F. A process for daily fine-tuning of the base LLM on all new research papers.