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Microsoft Developing AI-Enabled Database Solutions Sample Questions (Q60-Q65):

NEW QUESTION # 60
You are developing an Azure SQL database solution from a locally cloned GitHub repository by using Microsoft Visual Studio Code and GitHub Copilot Chat.
You need to ensure that GitHub Copilot Chat can call the hosted GitHub MCP Server tools by using OAuth. The MCP server configuration must be scoped to the repository.
What should you do in Visual Studio Code?

Answer: D

Explanation:
To connect GitHub Copilot Chat to an Azure SQL Database using a hosted GitHub MCP (Model Context Protocol) Server, follow these steps. This setup ensures that Copilot can interact directly with your repository and database schema using OAuth.
Step 1: Configure the MCP Server in VS Code
You must set the scope to the Workspace level to ensure the server is restricted to your specific repository.
Open your locally cloned GitHub repository in VS Code.
Open the Command Palette (Ctrl+Shift+P or Cmd+Shift+P).
Type and select: MCP: Add Server.
Select HTTP (HTTP or Server-Sent Events).
Enter the URL: https://api.githubcopilot.com/mcp/
Crucial: When prompted for the configuration scope, select Workspace Settings.
This saves the config to .vscode/settings.json, binding the server to this repo.
Step 2: Authenticate via OAuth
Once the server is added, Copilot needs permission to act on your behalf.
Open the GitHub Copilot Chat panel.
You will see a notification or a prompt within the chat asking to Authorize the MCP server.
Follow the browser prompts to sign in and grant OAuth permissions.
This allows the hosted MCP server to "see" your repository's code and metadata.
Reference:
https://skywork.ai/skypage/en/The-Ultimate-Guide-to-GitHub's-MCP-Server:-Bridging-AI-and- Your-Codebase/1970685991323758592


NEW QUESTION # 61
You are creating a table that will store customer profiles.
You have the following Transact-SQL code.

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:
* The schema meets the security requirements for PII data. # Yes
* Administrators of the Azure SQL server can see all the rows in dbo.CustomerProfiles when they use an application. # No
* The masking rules will apply even when row-level security (RLS) filters out rows. # No The first statement is Yes because the design combines two relevant SQL security controls for personally identifiable information: Dynamic Data Masking (DDM) on sensitive columns such as FullName, EmailAddress, and PhoneNumber, and Row-Level Security (RLS) to restrict which rows a user can access based on RegionCode. Microsoft documents that DDM limits sensitive data exposure for nonprivileged users
, while RLS restricts row access according to the user executing the query. Together, these are valid and appropriate controls for protecting PII in Azure SQL Database.
The second statement is No . Administrative users can view unmasked data because administrative roles effectively have CONTROL, which includes UNMASK. However, that does not mean they automatically see all rows through the application query path defined by the RLS policy. The security policy filters rows based on SUSER_SNAME() and matching RegionCode, so row visibility is governed by the predicate unless the policy is altered or bypassed administratively. DDM and RLS solve different problems: DDM affects how returned values are shown, while RLS affects which rows are returned at all.
The third statement is No because masking only applies to data that is actually returned in the query result set.
Microsoft describes DDM as hiding sensitive data in the result set of a query . If RLS filters a row out, that row is not returned, so there is nothing left for masking to act on. In other words, RLS eliminates inaccessible rows first from the user's perspective, and DDM masks sensitive column values only on rows the user is allowed to see.


NEW QUESTION # 62
You have an Azure SQL database that contains tables named dbo.ProductDocs and dbo.ProductDocsEmbeddings. dbo.ProductDocs contains product documentation and the following columns:
- DocID (int)
- Title (nvarchar(200))
- Body (nvarchar(max))
- LastModified (datetime2)
The documentation is edited throughout the day.
dbo.ProductDocsEmbeddings contains the following columns:
- DocID (int)
- ChunkOrder (int)
- ChunkText (nvarchar(max))
- Embedding (vector(1536))
The current embedding pipeline runs once per night.
You need to ensure that embeddings are updated every time the underlying documentation content changes. The solution must NOT require a nightly batch process.
What should you include in the solution?

Answer: A

Explanation:
To ensure embeddings are updated immediately when the product information table changes, you should implement Azure SQL Database Change Tracking combined with Azure Functions triggered via Event Grid. This approach eliminates daily batch processes by enabling an event- driven architecture that triggers embedding regeneration only for modified rows.
Reference:
https://medium.com/@granthgharewal/why-change-tracking-needs-to-be-enabled-for-sql-trigger- azure-functions-7d230a669fb8


NEW QUESTION # 63
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?

Answer: C

Explanation:
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


NEW QUESTION # 64
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 # 65
......

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