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NEW QUESTION # 84
You have a Microsoft SQL Server 2025 database that contains a table named products. products contains two columns named description and embedding. embedding is generated from description.
You have an application named App1. App1 has a feature that uses the following query.
VECTOR_DISTANCE('cosine', @query_vector, embedding)
Users report that the feature is slow during peak usage times.
You discover that during peak usage times, the semantic search latency increases and CPU utilization spikes.
You need to reduce latency and CPU utilization related to the App1 feature.
What should you do?
Answer: B
Explanation:
The correct answer is C because the current query uses VECTOR_DISTANCE( ' cosine ' , @query_vector, embedding) , which performs an exact vector distance calculation . Microsoft explicitly documents that VECTOR_DISTANCE does not use a vector index , even when one exists. Exact k-nearest-neighbor searches can require scanning large numbers of vectors and calculating distances row by row, which explains the observed CPU spikes and increased semantic-search latency during peak load.
A vector index on products.embedding is specifically designed to accelerate nearest-neighbor search, and VECTOR_SEARCH is the Transact-SQL function that can use that approximate nearest-neighbor index when the index metric matches the query metric. Using METRIC = ' cosine ' preserves the same similarity metric currently used by App1 while reducing the amount of computation required.
The other choices are incorrect:
* A normalizes vectors but does not replace the expensive exact search with indexed ANN retrieval.
* B replaces semantic vector search with lexical full-text search and changes the feature's behavior.
* D a standard nonclustered B-tree index cannot accelerate vector-distance operations.
* E converting embeddings to VARBINARY(8000) removes native vector semantics and does not solve the performance problem.
Therefore, the exam-aligned solution is create a vector index and use VECTOR_SEARCH with cosine distance .
NEW QUESTION # 85
You have an Azure SQL database that contains a table named stores, stores contains a column named description and a vector column named embedding.
You need to implement a hybrid search query that meets the following requirements:
* Uses full-text search on description for the keyword portion
* Returns the top 20 results based on a combined score that uses a weighted formula of 60% vector distance and 40% full-text rank How should you configure the query components? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
For the vector portion, the correct choice is VECTOR_DISTANCE and order by distance ascending . The requirement is to build a combined weighted formula using the actual vector distance. Microsoft documents that VECTOR_DISTANCE returns the exact distance between two vectors. Since lower distance means greater similarity, ascending distance is the right direction for ranking. VECTOR_SEARCH is for ANN retrieval, but this hotspot specifically asks for a weighted formula based on distance , so VECTOR_DISTANCE is the appropriate operator.
For the keyword portion, the correct choice is CONTAINSTABLE on description and return ranked matches . Microsoft documents that CONTAINSTABLE returns a RANK column from 0 through 1000 , which is exactly what is needed for weighted scoring in a hybrid formula.
For the final ranking expression, the best choice is order by (distance * 0.6) + ((1.0 - RANK/1000.0) * 0.4) .
This works because vector distance is a lower-is-better metric, while full-text RANK is a higher-is-better metric. Dividing RANK by 1000 normalizes it to the documented range, and subtracting from 1.0 converts it into a lower-is-better term so both components can be combined consistently in one ascending score. This final step is a sound inference based on Microsoft's documented distance semantics and full-text rank range.
NEW QUESTION # 86
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 # 87
You are developing an Azure SQL solution by using Microsoft Visual Studio 2026. The solution uses a GitHub repository.
You plan to use GitHub Copilot Chat to access the GitHub repository tools by connecting to the GitHub MCP Server.
You need to configure Visual Studio to support the planned configuration. The solution must rely on OAuth to access the MCP server.
What should you create?
Answer: A
Explanation:
In this scenario, you need to create an mcp.json file. This file acts as the configuration bridge that tells Visual Studio 2026 and GitHub Copilot Chat how to connect to and communicate with the GitHub MCP Server.
Key Configuration Details
The mcp.json file is typically placed in your solution's root directory or a specific .mcp folder to enable project-specific tools Configuration Steps
1. Create the MCP Configuration File
You must define the connection to the GitHub MCP server so Copilot Chat can interact with your repository tools.
File Name: mcp.json
Location: Usually placed in %AppData%\Microsoft\VisualStudio\2026\mcp.json (or the project root depending on your specific extension settings).
Content:
{
"mcpServers": {
"github": {
"url": "https://api.githubcopilot.com/mcp/",
"auth": "OAuth"
}
}
}
2. Authenticate via OAuth
3. Connect Azure SQL in VS 2026
Reference:
https://www.workato.com/the-connector/mcp-server-tutorial/
NEW QUESTION # 88
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:
Explanation:
Verified Answer : =
* Support agents must see only the rows of their own TenantId # Create a row-level security (RLS) predicate that uses the user context.
* Support agents must see only the domain name portion of CustomerEmail # Configure dynamic data masking on selected columns.
Comprehensive and Detailed Explanation with all Developing AI-Enabled Database Solutions documents : = For the TenantId requirement, use Row-Level Security (RLS) . RLS is designed to restrict which rows a database principal can access. A security predicate can compare the row ' s TenantId with tenant information derived from the current user ' s session or security context, so support agents querying the same Tickets table see only rows belonging to their own tenant. This is the correct multitenant isolation mechanism because it is enforced by the database rather than relying on individual reporting tools or application-side filtering.
For CustomerEmail , use Dynamic Data Masking (DDM) . DDM controls how sensitive column values are displayed to users who do not have UNMASK permission. A partial masking rule can conceal the local/user- specific portion of an email address while exposing only the required portion, such as its domain, according to the masking configuration.
The remaining choices do not satisfy these specific requirements. Transparent Data Encryption (TDE) protects database files at rest but does not filter rows or selectively obscure query results. Always Encrypted protects sensitive values cryptographically and is appropriate when the database engine itself should not see plaintext, but it is not the right mechanism when support agents must deliberately see a controlled portion of an email address.
Final drag-and-drop selections:
* Own TenantId rows # Create a row-level security (RLS) predicate that uses the user context
* Only domain portion of CustomerEmail # Configure dynamic data masking on selected columns
NEW QUESTION # 89
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