You have an Azure SQL database That contains a table named dbo.Products, dbo.Products contains three columns named Embedding Category, and Price. The Embedding column is defined as VECTOR(1536).
You use Ai_GENERME_EMBEDOINGS and VECTOR_SEARCH to support semantic search and apply additional filters on two columns named Category and Price.
You plan to change the embedding model from text-embedding-ada-002 to text-embedding-3-smalL Existing rows already contain embeddings in the Embedding column.
You need to implement the model change. Applications must be able to use VECTOR_SEARCH without runtime errors.
What should you do first?

• A. Regenerate embeddings for the existing rows.
• B. Create a vector index on dbo.Products.Embedding.
• C. Normalize the vector lengths before storing new embeddings.
• D. Convert the Embedding column to nvacchar(mex).

You have an Azure SQL database.
You need to create a scalar user-defined function (UDF) that returns the number of whole years between an input parameter named 0orderDate and the current date/time as a single positive integer. The function must be created in Azure SQL Database. You write the following code.

What should you insert at line 05?

• A. RETURN DATEDIFF(year, GETDATE(), @OrderDate);
• B. DATEDIFF(month, @orderdate, GETDATE()) / 12
• C. DATEPART(year, GETDATE()) - DATEPART(year, @orderdate)
• D. RETURN DATEDIFF(year, @OrderDate, GETDATE());

You have an Azure SQL database named SalesDB that supports an ecommerce application. SalesDB contains a table named dbo.Orders that has a clustered index on a column named orderid.
dbo.Orders receives continuous OLTP inserts and updates during business hours.
Your analytics team runs hourly aggregate queries that scan dbo.Orders to calculate revenue trends for recent dates.
You need to improve the performance of the hourly analytics queries without significantly affecting OLTP throughput. The solution must meet the following requirements:
Support near-real-time (NRT) analytics on the dbo.Orders table.
Reduce read time when retrieving analytical data from dbo.Orders.
Support indexing only rows that match a predicate, such as Active = 1.
Which type of index should you use for each requirement? To answer, drag the appropriate index types to the correct requirements. Each index type may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.

You have a GitHub Codespaces environment that has GitHub Copilot Chat installed and is connected to a SQL database in Microsoft Fabric named DB1 DB1 contains tables named Sales.Orders and Sales.Customers.
You use GitHub Copilot Chat in the context of DB1 .
A company policy prohibits sharing customer Personally Identifiable Information (Pll), secrets, and query result sets with any Al service.
You need to use GitHub Copilot Chat to write and review Transact-SQL code for a new stored procedure that will join Sales.Orders to sales .Customers and return customer names and email addresses. The solution must NOT share the actual data in the tables with GitHub Copilot Chat.
What should you do?

• A. Ask GitHub Copilot Chat to generate the stored procedure by using schema details only.
• B. From Sales.Customers, paste several rows that include email addresses into a chat, so that GitHub Copilot Chat can infer edge cases.
• C. Provide the database connection string to GitHub Copilot Chat so that GitHub Copilot Chat can validate the stored procedure.
• D. Run a select statement that returns customer names and email addresses and provide the result set to GitHub Copilot Chat so that GitHub Copilot Chat can generate the stored procedure.