Your development team uses GitHub Copilot Chat in Microsoft SQL Server Management Studio (SSMS) to generate and run Transact-SQL queries against an Azure SQL database named DB1.
DB1 contains tables that store sensitive customer data.
You need to ensure that any Transact-SQL queries that run from GitHub Copilot Chat in SSMS are restricted by the same permissions as the developer's database login.
What prevents the GitHub Copilot Chat-run queries from accessing data beyond the developer's access?

• A.GitHub Copilot Chat runs queries in a read-only sandbox that is isolated from production database permissions.
• B.GitHub Copilot Chat runs queries by using the developer's database identity and permissions.
• C.GitHub Copilot Chat filters query results on the client side to remove rows the developer is unauthorized to see.
• D.GitHub Copilot Chat uses different row-level security (RLS) policies than the developer.

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Why is indexing important for vector search?

• A.Reduces storage
• B.Improves similarity search performance
• C.Encrypts data
• D.Enables backups

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You have an Azure SQL database that contains tables named dbo.Tickets and dbo.TicketNotes.
dbo.Tickets contains support tickets and dbo.TicketNotes contains ticket notes.
A retrieval query returns the top five relevant ticket notes for a user question.
You plan to implement a Retrieval Augmented Generation (RAG) pattern that meets the following requirements:
- Formats the retrieved relational data for large language model (LLM)
processing
- Sends the user question and retrieved context to an Azure OpenAI REST endpoint for chat completions
- Extracts the response text from the LLM response
Which Transact-SQL function should you use to extract the response text?

• A.JSON_MODIFY(@response, '$.result')
• B.OPENJSON(@response)
• C.JSON_QUERY(@response, '$.choices')
• D.JSON_VALUE(@response, '$.choices[0].message.content')

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You have an Azure SQL database that contains a table named dbo.orders, dbo.orders contains a column named createDate that stores order creation dates.
You need to create a stored procedure that filters Orders by CreateDate for a single calendar day. The solution must be SARGable.
How should you complete the Transact-SQL code? To answer, drag the appropriate values to the correct targets. Each value 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.

Correct Answer:

Explanation:

The correct SARGable pattern for filtering a single calendar day is to use a half-open date range :
o.CreateDate > = @StartDate
AND o.CreateDate < @EndDate
with:
SET @EndDate = DATEADD(day, 1, @StartDate)
This is the correct design because it keeps the function off the column and applies it only to the parameter.
That allows SQL Server and Azure SQL to use an index on CreateDate efficiently, which is the key requirement for a SARGable predicate. Microsoft documents DATEADD as the standard function for adding one day to a date value, which makes it the right way to derive the exclusive upper boundary for the next day.
The incorrect choices are the ones that wrap CreateDate in CONVERT(...), because expressions like:
CONVERT(char(10), CreateDate, 121) = ...
make the predicate non-SARGable and typically prevent efficient seeks on an index over CreateDate.
So the completed procedure is:
CREATE PROCEDURE dbo.usp_SearchOrders
@StartDate date
AS
BEGIN
SET NOCOUNT ON;
DECLARE @EndDate date;
SET @EndDate = DATEADD(day, 1, @StartDate);
SELECT o.CreateDate,
o.OrderId,
o.ShipDate
FROM dbo.Orders AS o
WHERE o.CreateDate > = @StartDate
AND o.CreateDate < @EndDate;
END;

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You need to design a generative AI solution that uses a Microsoft SQL Server 2025 database named DB1 as a data source. The solution must generate responses that meet the following requirements:
- Are grounded in the latest transactional and reference data stored in DB1
- Do NOT require retraining or fine-tuning the language model when the
data changes
- Can include citations or references to the source data used in the
response
Which scenario is the best use case for implementing a Retrieval Augmented Generation (RAG) pattern? More than one answer choice may achieve the goal. Select the BEST answer.

• A.summarizing free-form user input text
• B.training a custom language model on historical database data
• C.answering user questions based on company-specific knowledge
• D.generating marketing slogans based on user sentiment analysis

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