20250724 update vector product applicability, edit pass

Author: WilliamDAssafMSFT

docs/relational-databases/vectors/vectors-faq.md

@@ -61,7 +61,7 @@ If you have multiple columns that you want to use for generating embeddings, you
 - Create one embedding for each column, or
 - Concatenate the values of multiple columns into a single string and then generate a single embedding for that concatenated string.
 
-For more information about the two options and the related database design considerations, see [Efficiently and Elegantly Modeling Embeddings in Azure SQL and SQL Server](https://devblogs.microsoft.com/azure-sql/efficiently-and-elegantly-modeling-embeddings-in-azure-sql-and-sql-server/).
+For more information about the two options and the related database design considerations, see [Efficiently and Elegantly Modeling Embeddings](https://devblogs.microsoft.com/azure-sql/efficiently-and-elegantly-modeling-embeddings-in-azure-sql-and-sql-server/).
 
 ## What about re-ranking?
 
@@ -73,7 +73,7 @@ For more information, review [Enhancing Search Capabilities with Hybrid Search a
 
 AI Search (now AI Foundry) is a specialized service designed for advanced search scenarios, including vector search, natural language processing, and AI-driven insights. It provides a comprehensive set of features for building intelligent search applications, such as built-in support for various AI models, advanced ranking algorithms, and integration with other AI services.
 
-Azure SQL and SQL Server provide the ability to store any kind of data and run any kind of query: structured and unstructured, and to perform vector search on that data. It is a good choice for scenarios where you need to do search across all these data together, and you don't want to use a separate service for search that would complicate your architecture. Azure SQL and SQL Server offer critical enterprise security features to make sure data is always protected, such as row-level security (RLS), dynamic data masking (DDM), Always Encrypted, immutable ledger tables, and transparent data encryption (TDE).
+The SQL Database Engine provides the ability to store any kind of data and run any kind of query: structured and unstructured, and to perform vector search on that data. It is a good choice for scenarios where you need to do search across all these data together, and you don't want to use a separate service for search that would complicate your architecture. The SQL Database Engine offers critical enterprise security features to make sure data is always protected, such as row-level security (RLS), dynamic data masking (DDM), Always Encrypted, immutable ledger tables, and transparent data encryption (TDE).
 
 Here's an example of a single query that can be run in Azure SQL or SQL Server that combines vector, geospatial, structured and unstructured data all at once. The sample query retrieves the top 50 most relevant restaurants based on the description of the restaurant, the location of the restaurant, and the user's preferences, using vector search for the description and geospatial search for the location, filtering also by star numbers, number of reviews, category and so on: