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NEW QUESTION # 79
Hotspot Question
Your company has an ecommerce catalog in a Microsoft SQL Server 2025 database named SalesDB. SalesDB contains a table named products. products contains the following columns:
- product_id (int)
- product_name (nvarchar(200))
- description (nvarchar(max))
- category (nvarchar(50))
- brand (nvarchar(50))
- price (decimal)
- sku (nvarchar(40))
The description fields are updated daily by a content pipeline, and price can change multiple times per day.
You want customers to be able to submit natural language queries and apply structured filters for brand and price.
You plan to store embeddings in a new VECTOR(1536) column and use VECTOR_SEARCH(...
METRIC='cosine' ...).
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:
NEW QUESTION # 80
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 # 81
Hotspot Question
You have a SQL database in Microsoft Fabric named SalesDB that contains a table named dbo.Products.
You need to modify SalesDB to meet the following requirements:
- Create a vector index on the appropriate column.
- Use a supplied natural language query vector.
How should you complete the Transact-SQL code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 82
Hotspot Question
You have an Azure SQL database that contains a table named dbo.SupportTickets.
dbo.SupportTickets contains a JSON column named Payload and a datetime column CreatedAt.
You need to generate a report for the last seven days that meets the following requirements:
- Returns exactly one row per customer per day
- For each customer and day, returns the earliest ticket
- Includes the customer ID stored in Payload
How should you complete the Transact-SQL query? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 83
You have an Azure SQL database named DB1 that contains two tables named knowledgebase and query_cache. knowledge_base contains support articles and embeddings. query_cache contains chat questions, responses, and embeddings DB1 supports an Al-enabled chat agent.
You need to design a solution that meets the following requirements:
* Serializes the retrieved rows from knowledee_base
* Extracts the answer field from the response
* Extracts the embeddings to store in query_cache
You will call the external large language model (LLM) by using the sp_irwoke_external_re standpoint stored procedure.
Which Transact-SGL commands should you use for each requirement? To answer, drag the appropriate commands to the correct requirements. Each command may be used once, mote 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:
The correct mapping is:
* FOR JSON PATH
* JSON_VALUE
* JSON_QUERY
To serialize the retrieved rows from knowledge_base , the correct command is FOR JSON PATH .
Microsoft documents that FOR JSON formats query results as JSON, and PATH mode is the standard way to shape relational rows into JSON for downstream application or AI use.
To extract the answer field from the response , the correct command is JSON_VALUE because answer is a single scalar field . Microsoft states that JSON_VALUE is used to extract a scalar value from JSON text.
To extract the embeddings to store in query_cache , the correct command is JSON_QUERY because embeddings are returned as a JSON array , not a scalar. Microsoft states that JSON_QUERY extracts an object or array from JSON text, which is exactly the right behavior for an embeddings payload.
The unused options are not the best fit here:
* OPENJSON is mainly for shredding JSON into rows and columns.
* AI_GENERATE_CHUNKS is for chunking text, not extracting fields from a response payload.
* VECTOR_DISTANCE computes similarity between vectors and is unrelated to JSON extraction.
* FOR XML PATH produces XML, not JSON.
NEW QUESTION # 84
......
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