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Choose the best response for each of the questions.

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Semantic search uses text embeddings to determine result relevance. What is an embedding vector?

An array of n numbers that capture the text's meaning.

An array of n words that summarize the text's meaning.

An array of n text strings embedded in the text.

An application's text data is stored in an Azure Database for PostgreSQL flexible server. The application needs a vector database to store the text embeddings and perform a semantic search. What is the most straightforward database choice?

Use Azure Database for PostgreSQL.

Use Vector Database in Azure Cosmos DB for MongoDB.

Use Azure AI Search's vector store.

An application has stored embedding vectors in a PostgreSQL flexible server database and is ready to query them. The user has supplied a query string. What is the simplest way to run a semantic search?

The application calls a stored function to return ranked results.

Use Azure OpenAI Embeddings API in the application, and use the result as a query parameter to rank cosine distance.

Use Azure AI Search's integrated vectorization to generate the query embedding and use the SQL in-line.

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