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NEW QUESTION # 21
Drag and Drop Question
You have a Microsoft SQL Server 2025 database that contains a table named dbo.CustomerMessages. dbo.CustomerMessages contains two columns named MessageID (int) and MessageRaw (nvarchar(max)).
MessageRaw can contain a phone number in multiple formats, and some rows do NOT contain a phone number.
You need to write a single SELECT query that meets the following requirements:
- The query must return MessageID, RawNumber, DigitsOnly, and
PhoneStatus.
- RawNumber must contain the first substring that matches a phone-
number pattern, or NULL if no match exists.
- DigitsOnly must remove all non-digit characters from RawNumber, or
return NULL.
- PhoneStatus must return valid when a phone number exists in
MessageRaw, otherwise return Missing.
How should you complete the Transact-SQL query? 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:
NEW QUESTION # 22
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 # 23
You have an Azure SQL database that contains a table named dbo.ManualChunks.
dbo.ManualChunks contains product manuals.
A retrieval query already returns the top five matching chunks as nvarchar (max)text.
You need to call an Azure OpenAI REST endpoint for chat completions. The request body must include both the user question and the retrieved chunks.
You write the following Transact-SQL code.
What should you insert at line 22?
Answer: D
Explanation:
To construct a payload for the Azure OpenAI REST API within Azure SQL, you can use a Common Table Expression (CTE) to structure the messages and then collapse them into a single JSON object.
T-SQL Payload Generation
Key Implementation Details
Message Hierarchy: Azure OpenAI requires a messages array. By nesting a subquery with FOR JSON PATH inside the main SELECT, SQL Server creates the required JSON array of objects.
JSON Formatting: Using FOR JSON PATH, WITHOUT_ARRAY_WRAPPER on the outer selection ensures the root of your @payload is a clean JSON object {...} rather than a single- element array [{...}].
Context Injection: It is standard practice to prepend the retrieved manual chunks to the system message or a dedicated user message to provide the model with the necessary grounding data.
Assuming you have your retrieved chunks concatenated into a single @Context variable and the user's input in @UserQuestion, use the following logic:
DECLARE @UserQuestion NVARCHAR(MAX) = 'How do I reset the device?';
DECLARE @Context NVARCHAR(MAX) = 'Manual Chunk 1... Manual Chunk 2...'; -- Your top 5 chunks DECLARE @payload NVARCHAR(MAX);
-- Define the message structure for Chat Completions
WITH ChatMessages AS (
SELECT 'system' AS [role],
'You are a helpful assistant. Use these manual excerpts: ' + @Context AS [content] UNION ALL SELECT 'user' AS [role],
@UserQuestion AS [content]
)
-- Generate the final JSON body
SELECT @payload = (
SELECT
(SELECT [role], [content] FROM ChatMessages FOR JSON PATH) AS [messages],
0.7 AS [temperature],
800 AS [max_tokens]
FOR JSON PATH, WITHOUT_ARRAY_WRAPPER
);
-- Resulting @payload is now ready for sp_invoke_external_rest_endpoint PRINT @payload; Reference:
https://devblogs.microsoft.com/azure-sql/using-openai-rest-endpoints-with-azure-sql-database
NEW QUESTION # 24
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: D
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 # 25
You have an Azure Al Search service and an index named hotels that includes a vector Held named DescriptionVector.
You query hotels by using the Search Documents REST API.
You add semantic ranking to the hybrid search query and discover that some queries return fewer results than expected, and captions and answers are missing.
You need to complete the hybrid search request to meet the following requirements:
* Include more documents when ranking.
* Always include captions and answers.
Answer:
Explanation:
Explanation:
These are the correct selections for a hybrid query that uses semantic ranking in Azure AI Search.
Use k = 50 because Microsoft explicitly recommends that when you combine semantic ranking with vector queries , you should set k to 50 so the semantic ranker has enough candidates to rerank. If you use a smaller value such as 10, semantic ranking can receive too few inputs, which is exactly why some queries return fewer results than expected.
Use queryType = " semantic " because captions and answers are only available on semantic queries.
Microsoft documents that captions is valid only when the query type is semantic, and semantic answers are returned only for semantic queries.
Use captions = " extractive " because semantic captions are extractive passages pulled from the top-ranked documents. Microsoft's REST documentation states that the valid captions option here is extractive and that it defaults to none if not specified.
Use answers = " extractive " because semantic answers in Azure AI Search are extractive, not generated.
Microsoft documents that semantic answers are verbatim passages recognized as answers and the REST API lists extractive as the answer-return option.
NEW QUESTION # 26
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