An airline company wants to build a conversational AI assistant to answer customer questions about flight schedules, booking, and payments. The company wants to use large language models (LLMs) and…

Question

Options

• ATrain models on Amazon SageMaker Autopilot.
• BDevelop a Retrieval Augmented Generation (RAG) agent by using Amazon Bedrock.
• CCreate a Python application by using Amazon Q Developer.
• DFine-tune models on Amazon SageMaker Jumpstart.

• A
  6% (3)
• B
  57% (29)
• C
  12% (6)
• D
  25% (13)

Explanation

The airline company aims to build a conversational AI assistant using large language models (LLMs) and a knowledge base to create a text-based chatbot with minimal development effort. Retrieval Augmented Generation (RAG) on Amazon Bedrock is an ideal solution because it combines LLMs with a knowledge base to provide accurate, contextually relevant responses without requiring extensive model training or custom development. RAG retrieves relevant information from a knowledge base and uses an LLM to generate responses, simplifying the development process. Retrieval Augmented Generation (RAG) in Amazon Bedrock enables developers to build conversational AI applications by combining foundation models with external knowledge bases. This approach minimizes development effort by leveraging pre-trained models and integrating them with data sources, such as FAQs or databases, to provide accurate and contextually relevant responses.

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