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44. Frage
You have an Azure SQL database that supports the OLTP workload of an order-processing application.
During a 10-minute incident window, you run a dynamic management view query and discover the following:
- Session 72 is sleeping with open_transaction_count = 1.
- Multiple other sessions show blocking_session_id = 72 in
sys.dm_exec_requests.
- sys.dm_exec_input_buffer(72, NULL) returns only BEGIN TRANSACTION
UPDATE Sales.Orders.
Users report that updates to Sales.Orders intermittently time out during the incident window. The timeouts stop only after you manually terminate session 72.
What is a possible cause of the blocking?
Antwort: D
Begründung:
This sounds like a classic orphaned transaction scenario.
The session was in a sleeping state with an open transaction, meaning the application sent the BEGIN TRANSACTION and the UPDATE statement, but then dropped the ball. Because SQL Server never received a COMMIT or ROLLBACK, it held onto the exclusive (X) locks on the Sales Order rows indefinitely.
Any other session trying to touch those same rows was forced to wait, leading to the blocking and eventual timeouts reported by your users. Manually killing the session forced a rollback, finally releasing the locks.
Reference:
https://learn.microsoft.com/en-ie/answers/questions/100075/sleeping-sessions-with-old-open- transactions-issue
45. Frage
You have an Azure SQL database that contains tables named dbo.ProduetDocs and dbo.
ProductuocsEnbeddings. dbo.ProductOocs contains product documentation and the following columns:
* Docld (int)
* Title (nvdrchdr(200))
* Body (nvarthar(max))
* LastHodified (datetime2)
The documentation is edited throughout the day. dbo.ProductDocsEabeddings contains the following columns:
* Dotid (int)
* ChunkOrder (int)
* ChunkText (nvarchar(aax))
* Embedding (vector(1536))
The current embedding pipeline runs once per night
Vou need to ensure that embeddings are updated every time the underlying documentation content changes The solution must NOT ' equire a nightly batch process.
What should you include in the solution?
Antwort: D
Begründung:
The requirement is to ensure embeddings are updated every time the underlying content changes without relying on a nightly batch job. The right design is to enable change tracking on the source table so an external process can identify which rows changed and regenerate embeddings only for those rows. Microsoft documents that change detection mechanisms are used to pick up new and updated rows incrementally , which is the right pattern when you need near-continuous refresh instead of full nightly rebuilds.
This is better than:
* A. fixed-size chunking , which affects chunk strategy but not change detection.
* B. a smaller embedding model , which affects model cost/latency but not update triggering.
* C. table triggers , which would push embedding-maintenance logic directly into write operations and is generally not the best design for AI-processing pipelines. The question specifically asks for a solution that replaces the nightly batch requirement, not one that performs heavyweight work inline during every transaction.
46. Frage
Drag and Drop Question
You have an Azure SQL database that contains a table named Sales.Orders. Sales.Orders contains the following columns.
Reporting queries frequently repeat logic to calculate the number of days since an order was placed.
You need to create a scalar user-defined function (UDF) that returns the number of days between an input value of @OrderDate and the current date and time.
How should you complete the Transact-SQL code? 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:
47. Frage
You have an Azure SQL database that contains a table named dbo.ManualChunks. dbo.HonualChunks 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 endpomt for chat completions. The request body must include both the user question and theretiieved chunks.
You write the following Transact-SQL code.
What should you insert at line 22?
Antwort: A
Begründung:
The correct insertion at line 22 is FOR JSON PATH, WITHOUT_ARRAY_WRAPPER .
The request body for the Azure OpenAI chat completions call must be a single JSON object containing the messages array with both the system/user content and the retrieved chunks. Microsoft documents that FOR JSON PATH is the preferred way to shape JSON output, especially when you want precise control over nested property names like messages[0].role and messages[1] .content.
The key detail is WITHOUT_ARRAY_WRAPPER . By default, FOR JSON returns results enclosed in square brackets as a JSON array. Microsoft documents that WITHOUT_ARRAY_WRAPPER removes those brackets so a single JSON object is produced instead. That is exactly what is needed here for @payload, because the stored procedure is building one request body, not an array of request bodies.
48. Frage
You have an Azure SQL database that contains a table named Tickets_Embeddings. Tickets_Embeddings contains a VECTOR(1536) column.
Embeddings are generated by using text-embedding-ada-002 and the /openai/v1/embeddings endpoint.
After a review of the current environment, the following changes are requested:
The AI architect has recommended switching to text-embedding-3-small.
The security team requires authentication by using Microsoft Entra managed identities only. Storing API keys in an application is prohibited.
Which two actions should you include in the solution? Each correct answer presents a part of the solution.
NOTE: Each correct selection is worth one point.
Antwort: A,B
49. Frage
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