AWS Certified Generative AI Developer - Professional AIP-C01

By Amazon · Question Mode

QuestionQ26

Operational Efficiency and Optimization for GenAI Applications

A university has recently digitized a collection of archival documents, academic journals, and manuscripts. The university keeps the digital files in an AWS Lake Formation data lake.

The university employs a GenAI developer to build a solution that lets users search the digital files with text queries. The solution must return journal abstracts that are semantically similar to a user's query. Users must be able to search the digitized collection using text and metadata associated with the journal abstracts. The digitized-file metadata contains no keywords. The solution must match similar abstracts to each other based on the similarity of their text. The data lake contains fewer than 1 million files.

Which solution will satisfy these requirements with the LEAST operational overhead?

Explanation

Amazon Titan Embeddings produces vector representations that capture the semantic meaning of abstract text. Aurora PostgreSQL Serverless with the pgvector extension can store those embeddings, perform vector similarity searches, and retain associated metadata for filtering. Using Bedrock and Aurora Serverless avoids operating a custom SageMaker model endpoint and reduces database capacity-management overhead; pgvector is supported for vector search in Aurora PostgreSQL.

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