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NEW QUESTION # 100
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: Add the Microsoft.SqlServer.Dacpacs.Master NuGet package to the project.
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.
Reference:
https://learn.microsoft.com/en-us/sql/tools/sql-database-projects/concepts/system-objects
NEW QUESTION # 101
You have a Microsoft SQL Server 2025 database that contains a table named products. products contains two columns named description and embedding. embedding is generated from description.
You have an application named App1. App1 has a feature that uses the following query.
VECTOR_DISTANCE('cosine', @query_vector, embedding)
Users report that the feature is slow during peak usage times.
You discover that during peak usage times, the semantic search latency increases and CPU utilization spikes.
You need to reduce latency and CPU utilization related to the App1 feature.
What should you do?
Answer: D
Explanation:
The correct answer is C because the current query uses VECTOR_DISTANCE( ' cosine ' , @query_vector, embedding) , which performs an exact vector distance calculation . Microsoft explicitly documents that VECTOR_DISTANCE does not use a vector index , even when one exists. Exact k-nearest-neighbor searches can require scanning large numbers of vectors and calculating distances row by row, which explains the observed CPU spikes and increased semantic-search latency during peak load.
A vector index on products.embedding is specifically designed to accelerate nearest-neighbor search, and VECTOR_SEARCH is the Transact-SQL function that can use that approximate nearest-neighbor index when the index metric matches the query metric. Using METRIC = ' cosine ' preserves the same similarity metric currently used by App1 while reducing the amount of computation required.
The other choices are incorrect:
* A normalizes vectors but does not replace the expensive exact search with indexed ANN retrieval.
* B replaces semantic vector search with lexical full-text search and changes the feature's behavior.
* D a standard nonclustered B-tree index cannot accelerate vector-distance operations.
* E converting embeddings to VARBINARY(8000) removes native vector semantics and does not solve the performance problem.
Therefore, the exam-aligned solution is create a vector index and use VECTOR_SEARCH with cosine distance .
NEW QUESTION # 102
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.
Answer:
Explanation:
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.
NEW QUESTION # 103
Hotspot Question
You have an SDK-style SQL database project named MyDatabaseProject.sqlprojstored in a private GitHub repository. The repository contains the following GitHub Actions workflow.
The repository contains the AZURE_SQL_CONNECTION_STRING secrets.
The target is an Azure SQL database that allows access to Azure services and is configured to support mixed authentication.
The workflow runs successfully.
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:
NEW QUESTION # 104
You have an Azure SQL database that contains a table named dbo.SupportTickets. dbo.SupportTickets contains a JSON column named Payload and a datetime column CreatedAt.
You need to generate a report for the last seven days that meets the following requirements:
* Returns exactly one row per customer per day
* For each customer and day, returns the earliest ticket
* Includes the customer ID stored in Payload
How should you complete the Transact-SQL query? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
* CustomerId extraction # JSON_VALUE
* Ranking function # ROW_NUMBER
* Final filter # 1
The first selection is JSON_VALUE because CustomerId is stored as a scalar property in the JSON payload at
$.customer.id . Microsoft documents that JSON_VALUE extracts a scalar value from JSON text, whereas OPENJSON is used to shred JSON into relational rows/columns.
The second selection is ROW_NUMBER . The query partitions the data by:
CAST(t.CreatedAt AS date),
JSON_VALUE(t.Payload, ' $.customer.id ' )
and orders each partition by:
t.CreatedAt ASC
Microsoft documents that ROW_NUMBER() assigns sequential numbers starting at 1 within each partition .
Therefore, the earliest ticket for each customer on each day receives rn = 1 .
The third selection is therefore 1 , because filtering with:
WHERE rn = 1
returns exactly one row per customer per calendar day-the earliest ticket in that partition.
So the completed logic is:
WITH TicketRanks AS
(
SELECT
t.TicketId,
CAST(t.CreatedAt AS date) AS TicketDate,
JSON_VALUE(t.Payload, ' $.customer.id ' ) AS CustomerId,
t.CreatedAt,
ROW_NUMBER() OVER
(
PARTITION BY
CAST(t.CreatedAt AS date),
JSON_VALUE(t.Payload, ' $.customer.id ' )
ORDER BY t.CreatedAt ASC
) AS rn
FROM dbo.SupportTickets AS t
WHERE t.CreatedAt > = DATEADD(day, -7, SYSUTCDATETIME())
)
SELECT
TicketDate,
CustomerId,
TicketId,
CreatedAt
FROM TicketRanks
WHERE rn = 1
ORDER BY TicketDate, CustomerId;
Final hotspot selections:
* First dropdown: JSON_VALUE
* Second dropdown: ROW_NUMBER
* Third dropdown: 1
NEW QUESTION # 105
......
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