Q22Applications of Foundation Models
A company is building a chatbot that uses Amazon Lex and Amazon OpenSearch Service. The chatbot uses the company’s private data to answer questions. The company must convert the data into a vector representation before storing it in a database. Which type of foundation model (FM) meets these requirements?
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C 2
To convert text data into a vector representation (a necessary step for enabling semantic search or retrieval in systems like Amazon OpenSearch), the correct type of foundation model to use is a:
✅ Text embeddings model
This type of model:
Converts textual input into dense numerical vectors (embeddings)
Preserves semantic meaning, enabling similarity comparisons and relevant search
Is typically used in retrieval-augmented generation (RAG) and search applications
C 1
The company needs to:
Convert text data into vector representations.
Store those vectors in a database (such as a vector index in Amazon OpenSearch Service).
Use the data for question answering, likely as part of a RAG (Retrieval Augmented Generation) solution.
A text embeddings model is specifically designed to transform text into numerical vectors (embeddings) that capture semantic meaning.