Exam Name: Developing AI-Enabled Database Solutions
Certification Provider: Microsoft
Corresponding Certification: Microsoft Certified: SQL AI Developer
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Last Updated: Oct 11, 2026
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Microsoft DP-800 Exam Syllabus Topics:
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Reference: https://learn.microsoft.com/en-us/credentials/certifications/resources/study-guides/dp-800
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DP-800 exam is an important Microsoft Certification which can test your professional skills. Candidates want to pass the exam successfully to prove their competence. Fast2test Microsoft technical experts have collected and certified 89 questions and answers of Microsoft Certified: SQL AI Developer - Developing AI-Enabled Database Solutions which are designed to cover the knowledge points of the Planning and Designing Microsoft Superdome Server Solutions and enhance candidates' abilities. With Fast2test DP-800 preparation tests you can pass the Microsoft Certified: SQL AI Developer - Developing AI-Enabled Database Solutions easily, get the Microsoft certification and go further on Microsoft career path.
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Microsoft Developing AI-Enabled Database Solutions Sample Questions:
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;
You are developing an Azure SQL database solution from a locally cloned GitHub repository by using Microsoft Visual Studio Code and GitHub Copilot Chat.
You need to ensure that GitHub Copilot Chat can call the hosted GitHub MCP Server tools by using OAuth.
The MCP server configuration must be scoped to the repository.
What should you do in Visual Studio Code?
- A. From the Command Palette, enter MCP: add server, select HTTP (HTTP or Server-Sent Events), enter
[https://api.githubcopilot.com/mcp/](https://api.githubcopilot.com/mcp/), and then save the configuration to the user settings. - B. Create a personal access token (PAT) that has the repo scope and store the PAT in the .github\mcp.json file as the Authorization header.
- C. Create a personal access token (PAT) that has the repo scope and store the PAT in the .vscode\mcp.json file as the Authorization header.
- D. From the Command Palette, enter MCP: add server, select HTTP (HTTP or Server-Sent Events), enter
[https://api.githubcopilot.com/mcp/](https://api.githubcopilot.com/mcp/), and then save the configuration to the workspace settings.
Correct Answer: D 🗳️
You have an Azure SQL database that contains a table named dbo.ManualChunks. dbo.HonualChunks contains product manuals A retrieval query already returns the top five matching chunks as nvarchar(max) text.
You need to call an Azure OpenAI REST endpomt for chat completions. The request body must include both the user question and theretiieved chunks.
You write the following Transact-SQL code.
What should you insert at line 22?
- A. FOR XHL PATH, INCLUDE_NULL_VAIUES
- B. FOR JSON PATH, WITHOUT_ARRAY_WRAPPER
- C. FOR XHL AUTO, TYPE, XML SCHEMA,
- D. FOR 3SON AUTO, IMCLUDE_MULL_VAIUES
Correct Answer: B 🗳️
Explanation: Only visible for Fast2test members. You can sign-up / login (it's free).
You have an Azure SQL database that contains a table named Tickets_Embeddings. Tickets_Embeddings contains a VECTOR(1536) column.
Embeddings are generated by using text-embedding-ada-002 and the /openai/v1/embeddings endpoint.
After a review of the current environment, the following changes are requested:
The AI architect has recommended switching to text-embedding-3-small.
The security team requires authentication by using Microsoft Entra managed identities only. Storing API keys in an application is prohibited.
Which two actions should you include in the solution? Each correct answer presents a part of the solution.
NOTE: Each correct selection is worth one point.
- A. Add a firewall rule.
- B. Change the primary keys for the tickets.
- C. Change the endpoint.
- D. Alter the database tables.
- E. Regenerate all the embeddings.
Correct Answer: C,E 🗳️
You have an Azure SQL database that contains the following tables and columns.
Embeddings in the NotesEnbeddings and DescriptionEabeddings tables have been generated from values in the Description and notes columns of the Articles table by using different chunk sizes.
You need to perform approximate nearest neighbor (ANN) queries across both embedding tables. The solution must minimize the impact of using different chunk sizes.
What should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
The correct function is VECTOR_SEARCH because the requirement is to perform approximate nearest neighbor (ANN) queries. Microsoft's SQL documentation states that VECTOR_SEARCH is the function used for vector similarity search, and that an ANN index is used only with VECTOR_SEARCH when a compatible vector index exists on the target column. By contrast, VECTOR_DISTANCE calculates an exact distance and does not use a vector index for ANN retrieval.
The correct distance metric is cosine distance. Microsoft documents that VECTOR_SEARCH supports cosine, dot, and euclidean metrics, and Microsoft guidance specifically notes that cosine similarity is commonly used for text embeddings. It also states that retrieval of the most similar texts to a given text typically functions better with cosine similarity, and that Azure OpenAI embeddings rely on cosine similarity to compute similarity between a query and documents. Since both NotesEmbeddings and DescriptionEmbeddings are text-derived embeddings and the goal is to minimize the impact of different chunk sizes, cosine is the best choice because it compares direction/angle rather than being as sensitive to vector magnitude as Euclidean distance.
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