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NEW QUESTION # 75
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 # 76
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You have a SQL database in Microsoft Fabric that contains a table named dbo.Orders.
dbo.Orders has a clustered index, contains three years of data, and is partitioned by a column named OrderDate by month.
You need to remove all the rows for the oldest month. The solution must minimize the impact on other queries that access the data in dbo.Orders.
Solution: Run the following Transact-SQL statement.
DELETE FROM dbo.Orders
WHERE OrderDate < DATEADD(month, -36, SYSUTCDATETIME());
Does this meet the goal?
Answer: A
Explanation:
Correct:
* Identify the partition number for the oldest month, and then run the following Transact-SQL statement.
TRUNCATE TABLE dbo.Orders
WITH (PARTITIONS (partition number));
The best Transact-SQL statement to remove all rows for the oldest month while minimizing the impact on other queries is TRUNCATE TABLE with a WITH (PARTITIONS (...)) clause.
Why TRUNCATE TABLE ... WITH (PARTITIONS (...)) is Best
Efficiency: TRUNCATE TABLE is a Data Definition Language (DDL) operation that removes data by deallocating the data pages, which is a metadata operation and is very fast, regardless of the amount of data in the partition.
Minimal Logging: It uses less transaction log space compared to a DELETE statement, which logs each row deletion individually.
Low Impact on Concurrency: It performs a quick, partition-specific operation. A row-by-row DELETE would be a long-running transaction and could cause locking and blocking issues for other queries accessing the table.
Data Integrity: Because the table has a clustered index and is partitioned by the same column (aligned indexes), the TRUNCATE PARTITION operation is a fast, partition-level maintenance operation that targets only that specific data subset.
Incorrect:
* : Identify the partition scheme for the oldest month, and then run the following Transact-SQL statement.
ALTER TABLE dbo.Orders
DROP PARTITION SCHEME (partition_scheme_name);
The DROP PARTITION SCHEME statement removes the partition scheme object from the database but does not remove the data itself or free up the space, and it requires all tables to be moved off the scheme first, which is a complex operation. This does not meet the goal of removing the data efficiently.
* Run the following Transact-SQL statement.
DELETE FROM dbo.Orders
WHERE OrderDate < DATEADD(month, -36, SYSUTCDATETIME());
A standard DELETE statement, even with a WHERE clause that uses the partition column, can be a time-consuming, logged operation that causes locking and blocking on the main table, negatively impacting performance.
Reference:
https://stackoverflow.com/questions/63632963/truncate-partition-vs-drop-partition-performace- wise-which-one-is-efficient-an
NEW QUESTION # 77
You have an Azure SQL database that contains tables named dbo.Tickets and dbo.TicketNotes.
dbo.Tickets contains support tickets and dbo.TicketNotes contains ticket notes.
A retrieval query returns the top five relevant ticket notes for a user question.
You plan to implement a Retrieval Augmented Generation (RAG) pattern that meets the following requirements:
- Formats the retrieved relational data for large language model (LLM)
processing
- Sends the user question and retrieved context to an Azure OpenAI REST endpoint for chat completions
- Extracts the response text from the LLM response
Which Transact-SQL function should you use to extract the response text?
Answer: A
Explanation:
The Transact-SQL function used to extract the response text from the Azure OpenAI REST endpoint is JSON_VALUE.
When you call the Azure OpenAI REST endpoint using sp_invoke_external_rest_endpoint, the response is returned as a JSON string. To isolate the actual text content from the LLM, you must parse this JSON structure.
Function: JSON_VALUE(response_body, '$.choices[0].message.content')
Purpose: It extracts a scalar (text) value from a JSON string.
Path: In the OpenAI schema, the generated response is always located at
$.choices[0].message.content.
Reference:
https://pub.towardsai.net/mastering-retrieval-augmented-generation-from-zero-to-expert-in-rag- for-quickly-building-a-08141a308836
NEW QUESTION # 78
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?
Answer: C
Explanation:
The best explanation is an open explicit transaction . During the incident, session 72 was sleeping but still had open_transaction_count = 1 , and sys.dm_exec_input_buffer(72, NULL) showed only BEGIN TRANSACTION UPDATE Sales.Orders. That pattern indicates the session executed an update inside an explicit transaction and then remained idle without committing or rolling back , while still holding locks.
Other sessions showing blocking_session_id = 72 is the expected symptom of that situation. Microsoft explains that blocking occurs when one session holds a lock on a resource and another session requests a conflicting lock, and sleeping sessions can continue to block if they retain locks through an open transaction.
This also fits the observed behavior that the timeouts stopped only after session 72 was terminated . Killing the session would roll back the active transaction and release the locks, allowing waiting updates to continue.
That is much more consistent with an uncommitted transaction than with a deadlock, because deadlocks are normally detected and one session is chosen as the victim automatically rather than persisting until manual termination.
NEW QUESTION # 79
You have a SQL database in Microsoft Fabric that contains a table named dbo.Orders, dbo.Orders has a clustered index, contains three years of data, and is partitioned by a column named OrderDate by month.
You need to remove all the rows for the oldest month. The solution must minimize the impact on other queries that access the data in dbo.orders.
Solution; Identify the partition scheme (or the oldest month, and then run the following Transact-SQL statement.
ALTER TABLE dbo.Orders
DROP PARTITION SCHEME (partition_scheme_name);
Does this meet the goal?
Answer: A
Explanation:
This also does not meet the goal. DROP PARTITION SCHEME removes the partition scheme object from the database; it is not the command used to remove just the rows for the oldest month from a partitioned table.
Microsoft's DROP PARTITION SCHEME documentation is explicit that the statement removes the partition scheme itself.
For removing only the oldest month's rows with minimal impact, Microsoft points to partition-level maintenance operations such as truncating a single partition on a partitioned table. That targets only the needed data subset and is more efficient for retention workloads.
NEW QUESTION # 80
......
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