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NEW QUESTION # 66
You have a SQL database in Microsoft Fabric that uses the default settings for a newly created database and contains a table named Sales.Orders.
You have an application that uses two stored procedures to access Sales.Orders.
While monitoring database activity, you discover the following:
* sys.dm_exec_requests shows multiple sessions in a suspended state with wait_type = LCK_M_X. All the sessions show the same wait_resource, which maps to Sales.Orders, and the same nonzero blocking_session_id.
* sys.dm_exec_input_buffer(blocking_session_id, NULL) returns a last submitted command of BEGIN TRANSACTION UPDATE Sales.Orders.
* sys.dm_exec_sessions for the blocking session shows status = sleeping and open_transaction_count = 1.
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:
Explanation:
The blocking is caused by an uncommitted explicit transaction in the blocking session that is holding locks. # Yes While an UPDATE operation on Sales.Orders is occurring, SELECT statements will be blocked. # No Joining sys.dm_tran_locks to sys.dm_exec_requests will show which session holds locks involved in the blocking chain. # Yes Comprehensive and Detailed Explanation with all Developing AI-Enabled Database Solutions documents: = The first statement is Yes. The evidence is conclusive: the blocking session is sleeping, has open_transaction_count = 1, and its last submitted command was BEGIN TRANSACTION UPDATE Sales.
Orders. Microsoft specifically identifies a sleeping session with an open transaction as a classic blocking condition: modifications retain their exclusive locks until the transaction is committed or rolled back. The LCK_M_X waits and identical nonzero blocking_session_id values confirm that other requests are waiting for incompatible exclusive locks.
The second statement is No because READ_COMMITTED_SNAPSHOT is ON by default for SQL database in Microsoft Fabric. Under the default READ COMMITTED isolation level, reads therefore use row versioning rather than shared locks. Consequently, a normal SELECT can read the appropriate committed row version while an UPDATE holds locks, so the writer does not ordinarily block the reader. Microsoft explicitly documents RCSI as ON by default in SQL database in Microsoft Fabric.
The third statement is Yes in the context of blocking diagnostics. sys.dm_tran_locks exposes lock ownership through fields such as request_session_id, request_mode, and request_status, while sys.dm_exec_requests exposes active request and blocking_session_id information. Correlating these DMVs allows you to associate locks with sessions participating in a blocking chain. Microsoft specifically recommends sys.dm_tran_locks for determining which locks are currently held and sys.dm_exec_requests for identifying blocked and blocking sessions.
NEW QUESTION # 67
Case Study 1 - Contoso
Existing Environment
Azure Environment
Contoso has an Azure subscription in North Europe that contains the corporate infrastructure.
The current infrastructure contains a Microsoft SQL Server 2017 database. The database contains the following tables.
The FeedbackJsoncolumn has a full-text index and stores JSON documents in the following format.
The support staff at Contoso never has the UNMASKpermission.
Problem Statements
Contoso is deploying a new Azure SQL database that will become the authoritative data store for the following:
* AI workloads
* Vector search
* Modernized API access
* Retrieval Augmented Generation (RAG) pipelines
Sometimes the ingestion pipeline fails due to malformed JSON and duplicate payloads.
The engineers at Contoso report that the following dashboard query runs slowly.
You review the execution plan and discover that the plan shows a clustered index scan.
VehicleIncidentReportsoften contains details about the weather, traffic conditions, and location. Analysts report that it is difficult to find similar incidents based on these details.
Requirements
Planned Changes
Contoso wants to modernize Fleet Intelligence Platform to support AI-powered semantic search over incident reports.
Security Requirements
Contoso identifies the following security requirements:
* Restrict the support staff from viewing Personally Identifiable Information (PII) data, which is full email addresses and phone numbers.
* Enforce row-level filtering so that analysts see only incidents for the fleets to which they are assigned. The analysts can be assigned to multiple fleets.
Database Performance and Requirements
Contoso identifies the following telemetry requirements:
* Telemetry data must be stored in a partitioned table.
* Telemetry data must provide predictable performance for ingestion and retention operations.
* latitude, longitude, and accuracyJSON properties must be filtered by using an index seek.
Contoso identifies the following maintenance data requirements:
* Ensure that any changes to a row in the MaintenanceEventstable updates the corresponding value in the LastModifiedUtccolumn to the time of the change.
* Avoid recursive updates.
AI Search, Embeddings, and Vector Indexing
Contoso plans to implement semantic search over incident data to meet the following requirements:
* Embeddings must be stored in dedicated Azure SQL Database tables.
* Embeddings must be generated from rich natural language fields.
* Chunking must preserve semantic coherence.
* Hybrid search must combine the following:
- Vector similarity
- Keyword filtering or boosting
Development Requirements
The development team at Contoso will use Microsoft Visual Studio Code and GitHub Copilot and will retrieve live metadata from the databases.
Contoso identifies the following requirements for querying data in the FeedbackJsoncolumn of the CustomerFeedbacktable:
* Extract the customer feedback text from the JSON document.
* Filter rows where the JSON text contains a keyword.
* Calculate a fuzzy similarity score between the feedback text and a known issue description.
* Order the results by similarity score, with the highest score first.
Hotspot Question
You need to create a table in the database to store the telemetry data.
You have the following Transact-SQL code.
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 # 68
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 # 69
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: A
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 # 70
You have an Azure SQL database that supports an AI-enabled product catalog API. The database contains a table named Sales.Orders. Sales.Orders contains 20 million rows and has a nonclustered index on a column named CreateDate. The API passes a date parameter named @OrderDate. You have a stored procedure named get_latest_day_orders that filters by running the following query: WHERE CAST(CreateDate AS date) =
@OrderDate A review of the query execution reveals that the query causes an index scan. You need to modify the predicate to ensure that Microsoft SQL Server can use an index seek on CreateDate for a single day. Which WHERE clause should you use?
Answer: D
NEW QUESTION # 71
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