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AIF-C01 practice question: RAG, knowledge bases and vector stores (3 of 4)

Applications of Foundation Models A free question from our original AIF-C01 practice exam. Try it, then open the answer to see why each option is right or wrong.

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A software company already runs Amazon Aurora PostgreSQL-Compatible Edition for its product catalog, and its database team knows it well.

A new RAG application needs embeddings stored and searched by similarity, and the company wants to avoid adopting a new database service.

Which option BEST meets these needs?

Choose one answer.

  1. A. Aurora PostgreSQL-Compatible Edition with the pgvector extension to store embeddings
  2. B. Amazon DynamoDB, with each embedding stored as a number list and retrieved by its partition key
  3. C. Aurora MySQL-Compatible Edition, with a full-text index built over the embedding text column
  4. D. Amazon S3, with each embedding saved as a separate JSON object in a bucket with versioning on
Show the answer and explanation

Answer: A

  • A. Right: Correct. Aurora PostgreSQL supports the pgvector extension, which stores vector embeddings and runs similarity search in the database the team already operates. This meets both the search need and the no-new-service constraint. Learn more: Amazon Aurora PostgreSQL now supports pgvector (opens in a new tab)
  • B. Wrong: Queries by partition key return exact key matches, not the nearest vectors. Similarity search would need extra design, and DynamoDB would be a new service for the team to learn.
  • C. Wrong: A full-text index finds documents that contain the query's words, not ones with similar meaning. It is not a vector search, so it would miss documents that express the same idea in different words.
  • D. Wrong: S3 stores objects durably but cannot search them by vector similarity. The application would have to load and compare the files itself.

Explanation

A vector store holds embeddings and finds the nearest ones to a query vector. Aurora PostgreSQL-Compatible Edition can do this with pgvector, so the team can keep its existing database and skills. Options that only store data or match keywords do not perform similarity search.

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