AWS Certified Cloud Practitioner

Explanation

Correct Answer: C - Use OpenSearch Serverless with properly sized capacity units and index refresh intervals

Why this is correct:

1. Bedrock Knowledge Base Architecture: Amazon Bedrock Knowledge Bases use vector databases (like OpenSearch Serverless) to store and retrieve embeddings efficiently. When query performance is slow, the vector database configuration is typically the bottleneck.

2. OpenSearch Serverless Optimization: Properly sizing capacity units ensures adequate compute resources for query processing, while optimizing index refresh intervals affects how frequently new data becomes searchable and impacts query performance.

3. Direct Impact on Retrieval Speed: Unlike the other options, this directly addresses the underlying infrastructure responsible for storing and querying embeddings.

Why other options are incorrect:

• A) Reduce model temperature: Model temperature affects the randomness/creativity of text generation, not retrieval speed from a knowledge base.

• B) Use a smaller embedding dimension size: While smaller embeddings might theoretically be faster, this would require re-embedding all data and could reduce semantic accuracy. It's not the most effective or practical solution.

• D) Add more S3 buckets: S3 buckets store source documents, not embeddings. Adding more buckets doesn't affect retrieval speed from the vector database.

Key Takeaway: When experiencing slow query performance with Bedrock Knowledge Bases, focus on optimizing the vector database configuration (OpenSearch Serverless) rather than model parameters or storage configurations.