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27. Frage
Drag and Drop Question
You have a database named DB1. The schema is stored in a GitHub repository as an SDK-style SQL database project.
You use a feature branch workflow to deploy changes to DB1.
You need to update the local feature branch with the latest changes to main, and then create a pull request to merge the feature branch into main for review.
How should you complete the GitHub CLI script? 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.
Antwort:
Begründung:
28. Frage
You need to generate embeddings to resolve the issues identified by the analysts. Which column should you use?
Antwort: D
Begründung:
The correct column to use for generating embeddings is incidentDescrlption because embeddings are intended to represent the semantic meaning of rich textual content , not simple categorical, numeric, or location-only values. Microsoft's DP-800 study guide explicitly includes skills such as identifying which columns to include in embeddings , generating embeddings , and implementing semantic vector search for scenarios where users need to find similar records based on meaning rather than exact matches.
In this scenario, analysts report that it is difficult to find similar incidents based on details such as weather, traffic conditions, and location . Those are descriptive context elements that are typically captured in a free- text incident description field. An embedding generated from incidentDescrlption can encode the semantic relationships among these narrative details, making it suitable for similarity search , semantic search , and RAG retrieval . Microsoft documentation on vectors and embeddings explains that embeddings are generated from text data and then stored for vector search to find semantically related items.
The other options are weaker choices:
* vehicleLocation is too narrow and usually better handled with geospatial filtering , not embeddings.
* incidentType is likely categorical and too low in semantic richness.
* SeverityScore is numeric and not appropriate as the primary source for semantic embeddings.
Microsoft also notes that when multiple useful attributes exist, you can either embed each text column separately or concatenate relevant text fields into one textual representation before generating the embedding.
But among the options given, the best and most exam-aligned answer is the textual narrative column :
incidentDescrlption .
29. Frage
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 ieferences 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.Mastet NuGet package to the project.
Does this meet the goal?
Antwort: A
Begründung:
The package named Microsoft.SqlServer.Dacpacs.Master is the generic master system DACPAC package, but the question requires the correct system objects for Azure SQL Database . Microsoft's system-objects documentation distinguishes platform-specific system references, and for Azure SQL Database the correct package is the Azure-specific master DACPAC , not the generic master package.
So adding Microsoft.SqlServer.Dacpacs.Master does not meet the goal for an Azure SQL Database- targeted SDK-style project. The expected package is the Azure-specific one.
30. Frage
You have a database named DB1. The schema is stored in a Git repository as an SDK-style SQL database project.
You have a GitHub Actions workflow that already runs dotnet build and produces a database artifact.
You need to add a deployment step that publishes the .dacpac file to an Azure SQL database by using the secrets stored in GitHub repository secrets.
What should you include in the workflow?
Antwort: D
Begründung:
To deploy your .dacpac to Azure SQL using GitHub Actions, you should use the official azure/sql- action@v2. This action is designed specifically to take the output of your dotnet build and publish it.
Assuming your build step is already working, here is how you structure the deployment job.
GitHub Actions Workflow Snippet
Add this job to your .yml file. It depends on your build job (usually named build) and runs on a runner with the Azure CLI installed (like ubuntu-latest).
- name: Deploy SQL Schema
uses: azure/sql-action@v2
with:
connection-string: ${{ secrets.AZURE_SQL_CONNECTION_STRING }}
path: './bin/Release/netstandard2.1/YourDatabase.dacpac' # Path to your .dacpac action: 'publish' Incorrect:
[Not A]
Use connection-string: ${{ secrets.AZURE_SQL_CONNECTION_STRING }}
[Not B]
Action set to publish, not to extract.
[Not D]
Use azure/sql-action@v2.
Reference:
https://stackoverflow.com/questions/75490960/github-actions-dotnet-publish-specify-a-project- with-a-dot-in-the-name
31. Frage
You need to design a generative AI solution that uses a Microsoft SQL Server 2025 database named DB1 as a data source. The solution must generate responses that meet the following requirements:
- Are grounded in the latest transactional and reference data stored in DB1
- 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.
Antwort: B
Begründung:
Implementing a Retrieval-Augmented Generation (RAG) pattern with Microsoft SQL Server 2025 allows you to ground generative AI responses in real-time company data without the need for model fine-tuning. This version of SQL Server functions as a native vector database, enabling seamless integration between relational data and AI-powered insights.
Incorrect:
[Not B]
Training a custom language model is not a use case for the Retrieval Augmented Generation (RAG) pattern.
In fact, the primary purpose of RAG is to provide a language model with up-to-date information without the need for training, retraining, or fine-tuning.
Reference:
https://devblogs.microsoft.com/azure-sql/sql-server-2025-embraces-vectors-setting-the- foundation-for-empowering-your-data-with-ai
32. Frage
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