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MLA-C02 practice question: Bedrock knowledge bases (2 of 2)

Deployment and Orchestration of ML and AI Workflows A free question from our original MLA-C02 practice exam. Try it, then open the answer to see why each option is right or wrong.

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A logistics broker keeps its carrier contract PDFs in Amazon S3 and runs an Amazon Aurora PostgreSQL cluster for its contract management system. The data team wants a Bedrock knowledge base over the contracts, and it does not want to create and pay for a separate vector database.

The knowledge base must store its embeddings in the existing cluster, and the database credentials must be kept in AWS Secrets Manager rather than in code or configuration files.

Which vector store configuration should the team choose?

Choose one answer.

  1. A. Create an Amazon OpenSearch Serverless collection for the embeddings, and move the contract management tables out of Aurora into it.
  2. B. Export the contract texts to CSV files in Amazon S3, and let the knowledge base answer questions over them through Amazon Athena queries.
  3. C. Put the database user name and password in the knowledge base prompt template, so that the knowledge base can connect to Aurora without a stored secret.
  4. D. Use the existing Aurora PostgreSQL cluster as the vector store with pgvector and the RDS Data API, and keep its credentials in Secrets Manager.
Show the answer and explanation

Answer: D

  • A. Wrong: OpenSearch Serverless is a supported vector store, but it is the separate vector database the broker wants to avoid, and moving the contract tables breaks the existing system. The stem asks to reuse the Aurora cluster.
  • B. Wrong: Athena is a query service and does not serve as a knowledge base vector store for semantic retrieval. The option does not use the vector store the stem needs.
  • C. Wrong: Placing credentials in a prompt template exposes them and breaks the stated Secrets Manager requirement. A prompt template does not provide database access.
  • D. Right: Correct. Bedrock knowledge bases support Aurora PostgreSQL as a vector store when pgvector is enabled, the RDS Data API is on, and the database user is managed in Secrets Manager. The existing cluster holds the embeddings, so no new vector database is needed. Learn more: Using Aurora PostgreSQL as a knowledge base for Amazon Bedrock (opens in a new tab)

Explanation

Bedrock knowledge bases can use an existing Aurora PostgreSQL cluster as the vector store, with the pgvector extension, the RDS Data API and a database user managed in Secrets Manager. A new OpenSearch collection adds the separate database the broker wants to avoid, Athena does not provide semantic retrieval, and credentials in a prompt template are unsafe.

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