Authoritative Microsoft - DP-800 - Test Developing AI-Enabled Database Solutions Practice

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Microsoft DP-800 Exam Syllabus Topics:

TopicDetails
Topic 1
  • Secure, optimize, and deploy database solutions: This domain focuses on implementing data security measures like encryption, masking, and row-level security, optimizing query performance, managing CI
  • CD pipelines using SQL Database Projects, and integrating SQL solutions with Azure services including Data API builder and monitoring tools.
Topic 2
  • Design and develop database solutions: This domain covers designing and building database objects such as tables, views, functions, stored procedures, and triggers, along with writing advanced T-SQL code and leveraging AI-assisted tools like GitHub Copilot and MCP for SQL development.
Topic 3
  • Implement AI capabilities in database solutions: This domain covers designing and managing external AI models and embeddings, implementing full-text, semantic vector, and hybrid search strategies, and building retrieval-augmented generation (RAG) solutions that connect database outputs with language models.

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Free PDF Quiz 2026 DP-800: Efficient Test Developing AI-Enabled Database Solutions Practice

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Microsoft Developing AI-Enabled Database Solutions Sample Questions (Q70-Q75):

NEW QUESTION # 70
You have an Azure SQL database that contains a table named dbo.ManualChunks.
dbo.ManualChunks 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 endpoint for chat completions. The request body must include both the user question and the retrieved chunks.
You write the following Transact-SQL code.

What should you insert at line 22?

Answer: C

Explanation:
To construct a payload for the Azure OpenAI REST API within Azure SQL, you can use a Common Table Expression (CTE) to structure the messages and then collapse them into a single JSON object.
T-SQL Payload Generation
Key Implementation Details
Message Hierarchy: Azure OpenAI requires a messages array. By nesting a subquery with FOR JSON PATH inside the main SELECT, SQL Server creates the required JSON array of objects.
JSON Formatting: Using FOR JSON PATH, WITHOUT_ARRAY_WRAPPER on the outer selection ensures the root of your @payload is a clean JSON object {...} rather than a single- element array [{...}].
Context Injection: It is standard practice to prepend the retrieved manual chunks to the system message or a dedicated user message to provide the model with the necessary grounding data.
Assuming you have your retrieved chunks concatenated into a single @Context variable and the user's input in @UserQuestion, use the following logic:
DECLARE @UserQuestion NVARCHAR(MAX) = 'How do I reset the device?';
DECLARE @Context NVARCHAR(MAX) = 'Manual Chunk 1... Manual Chunk 2...'; -- Your top 5 chunks DECLARE @payload NVARCHAR(MAX);
-- Define the message structure for Chat Completions
WITH ChatMessages AS (
SELECT 'system' AS [role],
'You are a helpful assistant. Use these manual excerpts: ' + @Context AS [content] UNION ALL SELECT 'user' AS [role],
@UserQuestion AS [content]
)
-- Generate the final JSON body
SELECT @payload = (
SELECT
(SELECT [role], [content] FROM ChatMessages FOR JSON PATH) AS [messages],
0.7 AS [temperature],
800 AS [max_tokens]
FOR JSON PATH, WITHOUT_ARRAY_WRAPPER
);
-- Resulting @payload is now ready for sp_invoke_external_rest_endpoint PRINT @payload; Reference:
https://devblogs.microsoft.com/azure-sql/using-openai-rest-endpoints-with-azure-sql-database


NEW QUESTION # 71
You have a SQL database in Microsoft Fabric that contains a column named Payload. Payload stores customer data in JSON documents that have the following format:
JSON
{
" date " : " 2026-01-25 " ,
" customer_email " : " user@contoso.com " ,
...
}
Data analysis shows that some customers have subaddressing in their email address; for example, user1+promo@contoso.com.
You need to return a normalized email value that removes the subaddressing, for example, user1+promo@contoso.com must be normalized to user1@contoso.com.
Which Transact-SQL expression should you use?

Answer: B


NEW QUESTION # 72
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You have an SDK-style SQL database project stored in a Git repository. The project targets an Azure SQL database.
The CI build fails with unresolved reference errors when the project references system objects.
You need to update the SQL database project to ensure that dotnet build validates successfully by including the correct system objects in the database model for Azure SQL Database.
Solution: Build the project by running dotnet build -bl -flp:v=diag.
Does this meet the goal?

Answer: B

Explanation:
Correct:
* Add the Microsoft.SqlServer.Dacpacs.Azure.Master NuGet package to the project.
To resolve system reference errors in an SDK-style SQL project targeting Azure SQL Database, you need to add a reference to the Microsoft.SqlServer.Dacpacs.Azure.Master NuGet package.
In your .sqlproj file, include the following item group:
<ItemGroup>
<PackageReference Include="Microsoft.SqlServer.Dacpacs.Azure.Master" Version="1.60.0" />
</ItemGroup>
Why this works:
System Objects: Standard SDK-style projects don't automatically include system views (like sys.database_principals or sys.dm_db_resource_stats). This package provides the necessary metadata for the compiler.
Azure Specifics: It includes Azure-only system objects that aren't present in the standard master database dacpac used for on-premises SQL Server.
CI/CD Friendly: Since it is a NuGet package, the dotnet build command will automatically restore it during the CI process without requiring manual file paths or local installations of Visual Studio.
Incorrect:
* Add an artifact reference to the Azure SQL Database master.dacpac file.
* Add the Microsoft.SqlServer.Dacpacs.Master NuGet package to the project.
* Build the project by running dotnet build -bl -flp:v=diag.
Reference:
https://learn.microsoft.com/en-us/sql/tools/sql-database-projects/concepts/system-objects


NEW QUESTION # 73
Vou have a SQL database in Microsoft Fabric that contains a nvarchar(max) column named MessageText. An ID is always contained within the first paragraph of MessageText.
You need to write a Transact SQL query that uses REGEXP_SUBSTR to extract the ID from MessageText.
What should you include in the query?

Answer: D

Explanation:
Microsoft documents REGEXP_SUBSTR for Transact-SQL with the string_expression parameter as supporting character string types char, nchar, varchar, and nvarchar. For the regex functions, support for LOB types such as varchar(max) and nvarchar(max) is specifically called out for REGEXP_LIKE , REGEXP_COUNT , and REGEXP_INSTR up to 2 MB, but that support note is not listed for REGEXP_SUBSTR in the surfaced documentation. In exam terms, the safe and expected approach is to cast the nvarchar(max) column to nvarchar(4000) before calling REGEXP_SUBSTR.
This also fits the scenario detail that the ID is always contained within the first paragraph of MessageText.
Since the needed value is near the start of the text, narrowing the input to a non-LOB string type such as nvarchar(4000) is sufficient and avoids incompatibility concerns with nvarchar(max).
The other options are not appropriate:
* A STRING_ESCAPE(..., ' json ' ) is for JSON escaping, not regex extraction.
* C adding a case-sensitive collation changes comparison behavior, but it is not the required fix for REGEXP_SUBSTR on nvarchar(max).
* D TRY_CONVERT(varchar(max), ...) still leaves a MAX type and also risks unnecessary Unicode loss.


NEW QUESTION # 74
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?

Answer: B

Explanation:
GitHub Copilot Chat in SSMS acts as an extension of the user, meaning it does not have its own separate service account or elevated privileges.
It operates within the security context of your active connection. If your database login is restricted by Role-Based Access Control (RBAC), Row-Level Security (RLS), or specific DENY permissions on sensitive tables, Copilot cannot bypass those hurdles to fetch or manipulate data you couldn't otherwise access manually.
Reference:
https://learn.microsoft.com/en-us/ssms/github-copilot/chat


NEW QUESTION # 75
......

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