AWS Certified Cloud Practitioner

Explanation

Bedrock Knowledge Bases is the correct answer because:

1. Automatic Document Embedding: Bedrock Knowledge Bases automatically processes and embeds documents into vector representations without requiring users to write custom embedding code.

2. Vector Search Without Custom Code: It provides built-in vector search capabilities, allowing users to retrieve relevant information from their knowledge base without needing to implement custom vector search solutions.

3. Managed RAG (Retrieval-Augmented Generation): Knowledge Bases is specifically designed for RAG workflows where documents are ingested, embedded, and made searchable to provide context to foundation models.

4. Comparison with other options:

   • Bedrock Guardrails: Focuses on content filtering and safety controls, not document embedding/retrieval.

   • Bedrock Model Evaluation: Used for testing and comparing model performance, not document management.

   • Bedrock Converse API: Provides a unified API for interacting with models, but doesn't handle document embedding and retrieval.

5. Use Case: This feature is ideal for building applications that need to query large document collections, such as customer support systems, research assistants, or enterprise knowledge management tools.

Key Benefits:

• Reduces development complexity

• Handles document chunking and embedding automatically

• Provides semantic search capabilities out-of-the-box

• Integrates seamlessly with other Bedrock features for complete RAG solutions