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Microsoft Developing AI-Enabled Database Solutions Sample Questions (Q24-Q29):

NEW QUESTION # 24
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?

Answer: A

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 # 25
You have an Azure SQL database that contains a column named Notes.
A security review discovers that Notes contains sensitive data.
You need to ensure that the data is protected so that neither the stored values nor the query inputs reveal information about the actual data. The solution must prevent a user from inferring relationships or repetitions in the data based on the encrypted output Which should you use?

Answer: B

Explanation:
The requirement says the stored values and query inputs must both be protected, and users must not be able to infer relationships or repetitions in the data from the encrypted output. Microsoft documents that deterministic encryption always produces the same ciphertext for the same plaintext , which allows equality comparisons but also leaks patterns. By contrast, randomized encryption produces a different encrypted value each time for the same plaintext, which improves security and prevents pattern analysis based on repeated ciphertext values.
That makes randomized encryption the right choice here:
* It protects data at rest and in transit/query parameters under Always Encrypted's client-side encryption model.
* It prevents attackers from learning that the same plaintext value appears repeatedly, because repeated inputs do not produce repeated ciphertext.
Why the other options are wrong:
* A. Always Encrypted with secure enclaves adds richer confidential query support, but the key protection property the question is testing is the encryption type. The requirement to prevent inference from repeated ciphertext points specifically to randomized encryption .
* C. RLS controls row access, not value confidentiality.
* D. Deterministic encryption allows equality-based operations but leaks repetition patterns, which the question explicitly forbids.


NEW QUESTION # 26
You have a SQL database in Microsoft Fabric named Sales BD that contains a table named dbo.Products. You need to modify SalesBD to meet the following requirements:
* Create a vector index on the appropriate column.
* Use a supplied natural language query vector.
How should you complete the Transact-SQL code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:

The first correct selection is embedding because a vector index must be created on the vector column , not on a scalar distance column or a text column such as product_name. Microsoft's CREATE VECTOR INDEX documentation shows that the index is created directly on the vector-valued column, for example ON product_embeddings(embedding).
The second correct selection is VECTOR_SEARCH because the requirement is to use a supplied natural language query vector and search against the indexed embeddings. Microsoft documents that VECTOR_SEARCH is the Transact-SQL function for approximate nearest neighbor vector retrieval and that it applies to SQL database in Microsoft Fabric as well as other supported SQL platforms.
This also matches the shown code pattern:
* declare a vector variable such as @query_vector VECTOR(1536),
* create a vector index on dbo.Products(embedding),
* query with VECTOR_SEARCH(... SIMILAR_TO = @query_vector, METRIC = ' cosine ' , TOP_N =
10).


NEW QUESTION # 27
You need to recommend a solution that will resolve the ingestion pipeline failure issues. Which two actions should you recommend? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.

Answer: B,E

Explanation:
The two correct actions are D and E because the ingestion failures are caused by malformed JSON and duplicate payloads , and these two controls address those two problems directly. Microsoft's JSON documentation states that SQL Server and Azure SQL support validating JSON with ISJSON , and Microsoft specifically recommends using a CHECK constraint to ensure JSON text stored in a column is properly formatted.
For the duplicate-payload issue, creating a unique index on a hash of the payload is the appropriate design.
Microsoft documents using hashing functions such as HASHBYTES to hash column values, and SQL Server allows a deterministic computed column to be used as a key column in a UNIQUE constraint or unique index . That makes a persisted hash-based computed column plus a unique index a practical and exam- consistent way to reject duplicate payloads efficiently.
The other options do not solve the stated root causes:
* Snapshot isolation addresses concurrency behavior, not malformed JSON or duplicate payload detection.
* A trigger to rewrite malformed JSON is not the right integrity control and is brittle.
* Foreign key constraints enforce referential integrity, not JSON validity or duplicate-payload prevention


NEW QUESTION # 28
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 # 29
......

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