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NEW QUESTION # 31
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 buildvalidates successfully by including the correct system objects in the database model for Azure SQL Database.
Solution: Add an artifact reference to the Azure SQL Database master.dacpac file.
Does this meet the goal?
Answer: A
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 # 32
You have an Azure SQL database named SalesDB
You have a Data API builder (DAB) instance that exposes the following entities in SalesDB
* A table entity named Order mapped to a table named dbo. Orders
* A stored procedure entity named FinalizeOrder mapped to a stored procedure named dbo.usp_FinalizeOrder The DAB runtime configuration includes the following permissions.
Client requests include a Microsoft Entra access token. The client also sends HTTP header x-MS-APl-ROlE:
operations for both REST and GraphQL requests.
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:
Explanation:
* A REST GET request to the order entity that includes the access token and x-MS-API-ROLE:
operations will return data. # No
* When DAB runs the stored procedure, the database policy defined on the FinalizeOrder entity will be enforced. # Yes
* If the client omits the x-MS-API-ROLE header but still sends the same access token, the order entity read request will run in the authenticated role context. # Yes The first statement is No . In Data API builder, when a valid token is sent with X-MS-API-ROLE, the request runs in that requested role if that role is present in the token . Here, that means the effective role becomes operations , not authenticated. But the order entity grants read only to the authenticated role, not to operations, so the GET request would not be authorized to return data.
The second statement is Yes . DAB evaluates the request against the permissions and policies configured for the effective role on the requested entity. The FinalizeOrder entity grants execute to role operations and includes a database policy of TenantId = @claims.tenantid, so that policy is part of the enforced authorization
/filtering behavior when the stored procedure entity is executed.
The third statement is Yes . If the client sends a valid access token without X-MS-API-ROLE, DAB uses the built-in Authenticated system role by default. Since the order entity allows read for the authenticated role, that read request runs in the authenticated role context.
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NEW QUESTION # 33
You need to design a generative Al solution that uses a Microsoft SOL Server 2025 database named DB1 as a data source. The solution must generate responses that meet the following requirements:
* Ait ' grounded In the latest transactional and reference data stored in D61
* Do NOT require retraining or fine-tuning the language model when the data changes
* Can include citations or references to the source data used in the response Which scenario is the best use case for implementing a Retrieval Augmented Generation (RAG) pattern?
More than one answer choice may achieve the goal. Select the BEST answer
Answer: C
Explanation:
The best use case for RAG is answering user questions based on company-specific knowledge . Microsoft defines RAG as a pattern that augments a language model with a retrieval system that provides grounding data at inference time, which is exactly what you need when responses must be based on the latest transactional and reference data , must avoid retraining/fine-tuning , and should be able to include citations or references to source data.
The other options do not fit as well:
* summarizing free-form user input does not inherently require retrieval from DB1,
* training a custom model contradicts the requirement to avoid retraining/fine-tuning,
* generating marketing slogans is a creative generation task, not a grounding-and-citation scenario. RAG is specifically strong when answers must come from your organization's own changing knowledge.
NEW QUESTION # 34
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: B
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 # 35
Case Study 1 - Contoso
Existing Environment
Azure Environment
Contoso has an Azure subscription in North Europe that contains the corporate infrastructure.
The current infrastructure contains a Microsoft SQL Server 2017 database. The database contains the following tables.
The FeedbackJsoncolumn has a full-text index and stores JSON documents in the following format.
The support staff at Contoso never has the UNMASKpermission.
Problem Statements
Contoso is deploying a new Azure SQL database that will become the authoritative data store for the following:
* AI workloads
* Vector search
* Modernized API access
* Retrieval Augmented Generation (RAG) pipelines
Sometimes the ingestion pipeline fails due to malformed JSON and duplicate payloads.
The engineers at Contoso report that the following dashboard query runs slowly.
You review the execution plan and discover that the plan shows a clustered index scan.
VehicleIncidentReportsoften contains details about the weather, traffic conditions, and location. Analysts report that it is difficult to find similar incidents based on these details.
Requirements
Planned Changes
Contoso wants to modernize Fleet Intelligence Platform to support AI-powered semantic search over incident reports.
Security Requirements
Contoso identifies the following security requirements:
* Restrict the support staff from viewing Personally Identifiable Information (PII) data, which is full email addresses and phone numbers.
* Enforce row-level filtering so that analysts see only incidents for the fleets to which they are assigned. The analysts can be assigned to multiple fleets.
Database Performance and Requirements
Contoso identifies the following telemetry requirements:
* Telemetry data must be stored in a partitioned table.
* Telemetry data must provide predictable performance for ingestion and retention operations.
* latitude, longitude, and accuracyJSON properties must be filtered by using an index seek.
Contoso identifies the following maintenance data requirements:
* Ensure that any changes to a row in the MaintenanceEventstable updates the corresponding value in the LastModifiedUtccolumn to the time of the change.
* Avoid recursive updates.
AI Search, Embeddings, and Vector Indexing
Contoso plans to implement semantic search over incident data to meet the following requirements:
* Embeddings must be stored in dedicated Azure SQL Database tables.
* Embeddings must be generated from rich natural language fields.
* Chunking must preserve semantic coherence.
* Hybrid search must combine the following:
- Vector similarity
- Keyword filtering or boosting
Development Requirements
The development team at Contoso will use Microsoft Visual Studio Code and GitHub Copilot and will retrieve live metadata from the databases.
Contoso identifies the following requirements for querying data in the FeedbackJsoncolumn of the CustomerFeedbacktable:
* Extract the customer feedback text from the JSON document.
* Filter rows where the JSON text contains a keyword.
* Calculate a fuzzy similarity score between the feedback text and a known issue description.
* Order the results by similarity score, with the highest score first.
You need to recommend a solution for the development team to retrieve the live metadata. The solution must meet the development requirements. What should you include in the recommendation?
Answer: B
Explanation:
Scenario: Development Requirements
The development team at Contoso will use Microsoft Visual Studio Code and GitHub Copilot and will retrieve live metadata from the databases.
To retrieve live metadata from Azure SQL databases and use it with GitHub Copilot in Visual Studio Code (VS Code), you must use the SQL Server (mssql) extension. This extension provides the native capability to extract a database schema as a .dacpac file directly within the editor.
1. Export the Schema as a .dacpac File
You can extract the schema of your live Azure SQL database using the SQL Server (mssql) extension.
2. Load the .dacpac into GitHub Copilot Context
Once the .dacpac file is saved in your VS Code workspace, you can provide it as context to GitHub Copilot Chat using #-mentions or Drag & Drop.
Reference:
https://learn.microsoft.com/en-us/sql/tools/sql-database-projects/concepts/data-tier- applications/extract-dacpac-from-database
NEW QUESTION # 36
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