Crucial Exams

AWS Certified AI Practitioner AIF-C01 Practice Question

A company builds a chatbot that uses retrieval-augmented generation (RAG). For each document, the team creates an embedding vector and stores it in a vector database. At runtime, the user query is also embedded, then a vector search is performed. What is the main purpose of this vector search step?

  • Identify documents whose embeddings are nearest to the query vector so semantically relevant content can be retrieved even when exact words differ.

  • Fine-tune the language model by adjusting its weights using the retrieved vectors.

  • Convert the document embeddings back into text tokens before they are sent to the language model.

  • Encrypt all stored embeddings with AES-256 to meet compliance requirements.

AWS Certified AI Practitioner AIF-C01
Fundamentals of Generative AI
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