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Microsoft DP-800 Exam Syllabus Topics:

Topic
Details

Topic 1

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 2

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 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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Microsoft Developing AI-Enabled Database Solutions Sample Questions (Q45-Q50):

NEW QUESTION # 45
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: Add the Microsoft.SqlServer.Dacpacs.Azure.Master NuGet package to the project.
Does this meet the goal?

A. No
B. Yes

Answer: B

Explanation:
This does meet the goal. Microsoft documents that SDK-style SQL projects can add the master.dacpac database reference as a package reference , and for Azure SQL Database the correct package is the Azure- specific master DACPAC package. The Azure SQL system DACPACs are available through NuGet, and this is the recommended way to include the right system objects in the database model for dotnet build validation.
So for an SDK-style SQL database project that targets Azure SQL Database, adding Microsoft.SqlServer.
Dacpacs.Azure.Master is the correct fix for unresolved references to system objects.

NEW QUESTION # 46
You need to meet the database performance requirements for maintenance data 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.

Answer:

Explanation:

Explanation:
* ON # m.maintenanceId = i.maintenanceId
* WHERE # m.LastModifiedUtc < > i.LastModifiedUtc
The correct drag-and-drop completion is:
* ON m.maintenanceId = i.maintenanceId
* WHERE m.LastModifiedUtc < > i.LastModifiedUtc
This satisfies the requirement to ensure that when a row in MaintenanceEvents changes, the corresponding LastModifiedUtc value is updated to the current system time, while also helping avoid unnecessary repeat updates.
The inserted pseudo-table in a SQL Server AFTER UPDATE trigger contains the rows that were just updated.
To update the matching row in the base table correctly, the trigger must join the target table row to the corresponding row in inserted by the table's primary key. In this schema, MaintenanceId is the primary key for MaintenanceEvents, so the correct join is m.maintenanceId = i.maintenanceId . Joining on VehicleId would be incorrect because multiple maintenance rows could exist for the same vehicle, which could update unintended rows. Microsoft's trigger documentation explains that inserted and deleted are used to work with the affected rows and that multi-row logic should be based on proper key matching.
The WHERE m.LastModifiedUtc < > i.LastModifiedUtc predicate is used to prevent the trigger from re- updating rows where the timestamp already matches the value in inserted. That reduces redundant writes and supports the requirement to avoid recursive or repeated update behavior. In practice, this means the trigger updates only rows whose current stored timestamp differs from the just-updated version. This is the exam- appropriate pattern for a self-updating timestamp column in an AFTER UPDATE trigger.

NEW QUESTION # 47
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. Create a vector index on dbo.Products.Embedding.
B. Regenerate embeddings for the existing rows.
C. Normalize the vector lengths before storing new embeddings.
D. Convert the Embedding column to nvacchar(mex).

Answer: B

Explanation:
When you change embedding models, the stored vectors should be treated as belonging to a different embedding space unless you intentionally keep the entire corpus consistent. Microsoft's vector guidance notes that when most or all embeddings are replaced with fresh embeddings from a new model, the recommended practice is to reload the new embeddings and, for large-scale replacement scenarios, consider dropping and recreating the vector index afterward so search quality remains predictable.
This question also says applications must continue to use VECTOR_SEARCH without runtime errors .
VECTOR_SEARCH requires compatible vector dimensions, and the vector column already exists. Azure OpenAI documentation shows that text-embedding-ada-002 is fixed at 1536 dimensions and text- embedding-3-small supports up to 1536 dimensions . That means the migration can remain compatible with a VECTOR(1536) column, but the right implementation step is still to re-embed the existing rows so the table does not contain a mixed corpus produced by different models.
The other options are not appropriate:
* B normalization does not solve a model migration problem.
* C converting the vector column to nvarchar(max) would break vector-native search design.
* D a vector index improves performance, but it does not migrate old embeddings to the new model.

NEW QUESTION # 48
You have an Azure Al Search service and an index named hotels that includes a vector Held named DescriptionVector.
You query hotels by using the Search Documents REST API.
You add semantic ranking to the hybrid search query and discover that some queries return fewer results than expected, and captions and answers are missing.
You need to complete the hybrid search request to meet the following requirements:
* Include more documents when ranking.
* Always include captions and answers.

Answer:

Explanation:

These are the correct selections for a hybrid query that uses semantic ranking in Azure AI Search.
Use k = 50 because Microsoft explicitly recommends that when you combine semantic ranking with vector queries , you should set k to 50 so the semantic ranker has enough candidates to rerank. If you use a smaller value such as 10, semantic ranking can receive too few inputs, which is exactly why some queries return fewer results than expected.
Use queryType = " semantic " because captions and answers are only available on semantic queries.
Microsoft documents that captions is valid only when the query type is semantic, and semantic answers are returned only for semantic queries.
Use captions = " extractive " because semantic captions are extractive passages pulled from the top-ranked documents. Microsoft's REST documentation states that the valid captions option here is extractive and that it defaults to none if not specified.
Use answers = " extractive " because semantic answers in Azure AI Search are extractive, not generated.
Microsoft documents that semantic answers are verbatim passages recognized as answers and the REST API lists extractive as the answer-return option.

NEW QUESTION # 49
You have an Azure SQL database that has Query Store enabled
Query Performance Insight shows that one stored procedure has the longest runtime. The procedure runs the following parameterized query.

The dbo.orders table has approximately 120 million rows. Customer-id is highly selective, and orderOate is used for range filtering and sorting.
Vou have the following indexes:
* Clustered index: PK_Orders on (Orderld)
* Nonclustered index: lx_0rders_order-Date on (OrderDate) with no included columns An actual execution plan captured from Query Store for slow runs shows the following:
* An index seek on ixordersorderDate followed by a Key Lookup (Clustered) on PKOrders for customerid, status, and TotalAnount
* A sort operator before top (50), because the results are ordered by orderDate DESC For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.

Answer:

Explanation:

The first statement is Yes . The query filters on CustomerId, applies a range predicate on OrderDate, and sorts by OrderDate DESC. Microsoft's index design guidance recommends putting equality predicates first in the key, followed by columns used for ordering/range access, because the order of key columns determines seek and sort support. A nonclustered index on (CustomerId, OrderDate DESC) can support an ordered seek for this query and avoid the explicit sort. Including Status and TotalAmount helps cover the query, and OrderId is already available because the clustered key is stored with nonclustered index rows.
The second statement is No . Adding CustomerId as an included column to IX_Orders_OrderDate does not make it part of the index's navigational structure. Microsoft states that included columns are nonkey columns used to cover queries; they do not provide the seek and ordering characteristics that key columns do. So an index keyed only on OrderDate still is not the right ordered access path for WHERE CustomerId =
@CustomerId ... ORDER BY OrderDate DESC.
The third statement is Yes . The described actual plan shows an index seek on the wrong access path for the workload, followed by clustered key lookups and an explicit sort before TOP (50). That is characteristic of a suboptimal query/index plan . Query Store and Query Performance Insight are designed to surface plan- related performance regressions, while locking/blocking problems are typically identified through waits
/DMVs and blocking-session indicators, not from a plan shape like seek + lookup + sort alone.

NEW QUESTION # 50
......

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