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| Section | Weight | Objectives |
|---|---|---|
| Design and develop database solutions (35–40%) | 35-40% | - Design and implement programmability objects
|
| Secure, optimize, and deploy database solutions (35–40%) | 35-40% | - Implement data platform resources
|
| Implement AI capabilities in database solutions (25–30%) | 25-30% | - Integrate Azure AI services
|
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NEW QUESTION # 46
Drag and Drop Question
You have an Azure SQL database named SalesDB. SalesDB contains a table named dbo.Articles. dbo.Articles contains the following columns:
- ArticleId
- Title
- Body
- LastModifiedUtc
- EmbeddingVector
You have an application that generates embeddings from the concatenation of Title and Body and stores the results in EmbeddingVector.
You plan to implement an incremental embedding maintenance method that will use change data capture (CDC) to update embeddings only for rows that change, without scanning the entire table.
You need to ensure that only the columns required to generate the embeddings are captured.
The solution must support querying net changes.
How should you complete the Transact-SQL 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.
Answer:
Explanation:
NEW QUESTION # 47
You have an Azure SQL database named SalesDB on a logical server named sales-sql01.
You have an Azure App Service web app named OrderApi that connects to SalesDB by using SQL authentication.
You enable a user-assigned managed identity named OrderApi-Id for OrderApi.
You need to configure OrderApi to connect to SalesDB by using Microsoft Entra authentication. The managed identity must have read and write permissions to SalesDB.
Which Transact-SQL statements should you run in SalesDB?
Answer: D
Explanation:
For an Azure App Service using a user-assigned managed identity to connect to Azure SQL Database with Microsoft Entra authentication , the required database-side step is to create a database user from the external provider , then grant the needed database roles. Microsoft's Azure SQL documentation for managed identities states that to let a managed identity access the target database, you create a SQL user for that identity by using:
CREATE USER [ < identity-name > ] FROM EXTERNAL PROVIDER;
and then assign the appropriate roles.
That makes db_datareader and db_datawriter the right role grants here, because the requirement says the identity must have read and write permissions to SalesDB.
The other options are incorrect:
* A uses CREATE LOGIN ... FROM EXTERNAL PROVIDER, which is not the right choice for this Azure SQL Database scenario; the documented pattern is to create a database user from the external provider.
* B and D create SQL-authentication principals with passwords, which does not meet the Microsoft Entra managed-identity requirement.
* D also grants sysadmin, which is a server-level overgrant and not appropriate for the stated read/write requirement.
NEW QUESTION # 48
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?
Answer: D
Explanation:
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.
NEW QUESTION # 49
You have an Azure SQL database that supports an OLTP application.
You need to write Transact-SQL code that returns blocking chain details. The output must return only sessions that ate blocked or are blocking other sessions.
How should you complete the 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.
Answer:
Explanation:
Explanation:
* CTE inner source # FROM sys.dm_exec_requests
* Join after sys.dm_exec_sessions AS s # LEFT OUTER JOIN sys.dm_exec_requests
* Text retrieval # OUTER APPLY sys.dm_exec_sql_text(r.sql_handle)
* Input buffer retrieval # OUTER APPLY sys.dm_exec_input_buffer(r.session_id, r.request_id) The correct drag-and-drop choices are based on how blocking-chain details are normally assembled in Azure SQL Database.
The CTE must read from sys.dm_exec_requests because the alias er is used with er.session_id and er.
blocking_session_id, and those columns come from sys.dm_exec_requests. Microsoft documents that sys.
dm_exec_requests returns information about executing requests and includes the blocking_session_id column used to identify blockers.
After FROM sys.dm_exec_sessions AS s, the correct join is LEFT OUTER JOIN sys.dm_exec_requests so the query can still return sessions from sys.dm_exec_sessions even when a current request row is missing.
This is useful when showing sessions that are blocked or blocking, while still attempting to attach current request details when available.
For batch text, use OUTER APPLY sys.dm_exec_sql_text(r.sql_handle) because Microsoft documents sys.
dm_exec_sql_text(sql_handle) as the function that returns the SQL batch text for the specified sql_handle.
For the input buffer, use OUTER APPLY sys.dm_exec_input_buffer(r.session_id, r.request_id) because Microsoft documents that sys.dm_exec_input_buffer takes session_id and request_id and returns event_info, which is commonly used when sys.dm_exec_sql_text is null or when you want the last command text.
So the completed code uses:
* FROM sys.dm_exec_requests
* LEFT OUTER JOIN sys.dm_exec_requests
* OUTER APPLY sys.dm_exec_sql_text(r.sql_handle)
* OUTER APPLY sys.dm_exec_input_buffer(r.session_id, r.request_id)
NEW QUESTION # 50
You have an Azure SQL database that contains a column named Notes.
A security review discovers that Notes contains sensitive data.
You need to ensure that the data is protected so that neither the stored values nor the query inputs reveal information about the actual data. The solution must prevent a user from inferring relationships or repetitions in the data based on the encrypted output Which should you use?
Answer: D
Explanation:
The requirement says the stored values and query inputs must both be protected, and users must not be able to infer relationships or repetitions in the data from the encrypted output. Microsoft documents that deterministic encryption always produces the same ciphertext for the same plaintext , which allows equality comparisons but also leaks patterns. By contrast, randomized encryption produces a different encrypted value each time for the same plaintext, which improves security and prevents pattern analysis based on repeated ciphertext values.
That makes randomized encryption the right choice here:
* It protects data at rest and in transit/query parameters under Always Encrypted's client-side encryption model.
* It prevents attackers from learning that the same plaintext value appears repeatedly, because repeated inputs do not produce repeated ciphertext.
Why the other options are wrong:
* A. Always Encrypted with secure enclaves adds richer confidential query support, but the key protection property the question is testing is the encryption type. The requirement to prevent inference from repeated ciphertext points specifically to randomized encryption .
* C. RLS controls row access, not value confidentiality.
* D. Deterministic encryption allows equality-based operations but leaks repetition patterns, which the question explicitly forbids.
NEW QUESTION # 51
......
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