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NEW QUESTION # 36
Your company has an ecommerce catalog in a Microsoft SQL Server 202b 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(W))
* price (decimal)
* sku (nvarchar(40))
The description fields ate updated dairy 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:
Explanation:
The first statement is Yes . Embeddings are used to represent the semantic meaning of content, and vector search is for conceptually similar matches over that content. Here, the semantically meaningful fields are product_name, category, and description. Using those together supports natural-language search, while brand and price can be handled as structured filters outside the embedding itself. This is an inference from Microsoft's guidance that vector search works over embeddings representing content meaning, while filters remain part of the nonvector query pipeline.
The second statement is No . price changes multiple times per day and is a structured numeric attribute, not stable semantic content. Since the requirement already says customers can apply structured filters for brand and price , price does not need to be embedded into the text. Embedding volatile numeric values would also make embeddings stale faster without improving the semantic-search objective. This is again an inference grounded in Microsoft's distinction between vector similarity over content and filtering/sorting over nonvector fields.
The third statement is Yes . In SQL Server's vector type, the default underlying base type is float32 unless float16 is specified explicitly.
NEW QUESTION # 37
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: B
Explanation:
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
NEW QUESTION # 38
Your development team uses Microsoft Visual Studio Code with the MSSQL extension and the GitHub Copilot Chat extension.
The team connects to an Azure SQL database by using individual database logins and uses the
@mssql chat participant to generate and run Transact-SQL queries from prompts.
What is used to ensure that GitHub Copilot Chat-generated queries run in the context of the developer?
Answer: A
Explanation:
To ensure that GitHub Copilot Chat-generated queries run in the context of a specific developer when using the @mssql chat participant, SQL Permissions must be used.
Why SQL Permissions are the Key
When the @mssql extension executes a query generated by Copilot, it uses the active connection currently established in VS Code. Because your team uses individual database logins, the execution context is governed by the following:
Authentication: The developer logs in with their specific credentials.
Authorization: The SQL Server engine checks the SQL Permissions (GRANT/DENY/REVOKE) assigned to that specific database user.
Execution: Any T-SQL command sent by the Copilot chat participant is limited by what that specific login is allowed to do (e.g., SELECT, UPDATE, or DROP).
Reference:
https://learn.microsoft.com/en-us/sql/tools/visual-studio-code-extensions/github-copilot/limitations- and-known-issues
NEW QUESTION # 39
Vou have a Microsoft Fabric workspace named Workspace1 that contains a SQL database named SalesDB and an API for GraphQL tern named SalesApi.
You have a Microsoft Entra group named SqlUsers.
From Workspace1, you assign permission to SalesApi as shown in the following exhibit.
The connection to SalesDB has the connectivity option configured as shown in the following exhibit.
SqlUsers has the Viewer role for Workspace1.
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 members of SqlUsers can modify the data in SalesDB via SalesApi. # No
* The members of SqlUsers can view the data in SalesDB via SalesApi. # No
* The members of SqlUsers can change the field mappings of SalesApi. # Yes For both viewing and modifying data through SalesApi, the key missing permission is Run Queries and Mutations . Microsoft's Fabric GraphQL documentation states that callers need Execute permissions for the GraphQL API , which correspond to the Run Queries and Mutations option, and with SSO connectivity they also need appropriate permissions on the underlying data source. In the exhibit, SqlUsers has View and Edit GraphQL item selected, but Run Queries and Mutations is not selected, so members cannot query or mutate data through the API.
The third statement is Yes because the group was explicitly granted View and Edit GraphQL item . That permission is the one that allows users to open and modify the GraphQL item itself, including schema-related configuration such as field mappings in the API item. The workspace Viewer role does not by itself grant query execution through the API, but the direct GraphQL item permission shown does allow editing the item.
NEW QUESTION # 40
Drag and Drop Question
You have an Azure SQL database named sqldb-ai-prod that stores customer support tickets for a multitenant software as a service (SaaS) application. sqldb-ai-prod contains a table named Tickets. Tickets contains columns named TenantId, TicketId, CustomerEmail, CustomerPhone, and Notes.
You plan to harden data access, since a new support team will use ad hoc reporting tools that connect directly to sqldb-ai-prod.
You need to configure security to meet the following requirements:
- Support agents must see only the rows of their own TenantId column.
- Support agents must see only the domain name portion of the
CustomerEmail column.
What should you do for each requirement? To answer, drag the appropriate actions to the correct requirements. Each action 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 # 41
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