AWS Certified Cloud Practitioner

Explanation:

For a financial services firm that needs to store and query millions of embeddings with full control over database configuration, the best choice is Amazon RDS for PostgreSQL with pgvector.

Here's why:

1. Self-Managed Control: Amazon RDS (Relational Database Service) allows customers to manage their own database instances while AWS handles the underlying infrastructure. This provides the "full control over database configuration" mentioned in the requirement.

2. pgvector Extension: PostgreSQL with the pgvector extension is specifically designed for vector similarity search and storage. It can efficiently handle millions of embeddings.

3. Comparison with Other Options:

   • Amazon Aurora with pgvector (Option A): While Aurora also supports pgvector, it's a managed database service with less configuration control than RDS.

   • Amazon OpenSearch Service (Option C): This is a managed service with less configuration control than self-managed RDS.

   • Amazon Neptune (Option D): Primarily a graph database, not optimized for vector storage.

   • Amazon DynamoDB (Option E): A NoSQL database not designed for vector similarity search.

   • Amazon MemoryDB for Redis (Option F): While Redis can store vectors, it's not as feature-rich as pgvector for vector similarity operations.

4. Integration with Amazon Bedrock: The pgvector extension in PostgreSQL can easily integrate with AI/ML workflows, making it suitable for powering an AI assistant built on Amazon Bedrock.

5. Financial Services Requirements: RDS provides better control over security configurations, backups, and compliance settings, which is crucial for financial services firms handling sensitive customer data.

The key phrase "self-manage" and "full control over database configuration" points directly to Amazon RDS rather than fully managed services like Aurora or OpenSearch.