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NEW QUESTION # 63
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: B
NEW QUESTION # 64
You have an Azure SQL database that contains the following SQL graph tables:
- A NODE table named dbo.Person
- An EDGE table named dbo.Knows
Each row in dbo.Person contains the following columns:
- PersonID (int)
- DisplayName (nvarchar(100))
You need to use a MATCH operator and exactly two directed Knows relationships to return the PersonID and DisplayName of people that are reachable from the person identified by an input parameter named @StartPersonId.
Which Transact-SQL query should you use?
Answer: B
Explanation:
To achieve this using SQL Graph in Azure SQL, you use the MATCH clause to define the path.
Since you need exactly two directed relationships (A → B → C), you must chain the edge table twice in the same direction.
Transact-SQL Statement
SELECT
Person3.ID,
Person3.NameColumn -- Replace with your actual nvarchar(100) column name FROM PersonNodeTable AS Person1, KnowsEdgeTable AS Knows1, PersonNodeTable AS Person2, KnowsEdgeTable AS Knows2, PersonNodeTable AS Person3 WHERE MATCH(Person1-(Knows1)->Person2-(Knows2)->Person3) AND Person1.ID = @InputID; Use code with caution.
Key Components
MATCH Clause: Defines the traversal pattern. The syntax (Node)-(Edge)->(Node) ensures the relationship is directed.
Chaining: To get "exactly two" steps, you define three node aliases and two edge aliases.
Aliases: Each instance of the table must have a unique alias (e.g., Person1, Person2, Person3) so the engine can distinguish between the different points in the path.
Filtering: The WHERE clause uses your input parameter (@InputID) to set the starting point of the graph traversal Incorrect:
[Not A]
Need three persons, not two.
[Not B]
Do not use JOIN.
[Not C]
Incorrect MATCH statement.
Reference:
https://learn.microsoft.com/en-us/sql/relational-databases/graphs/sql-graph-sample
NEW QUESTION # 65
You have an Azure SQL table that contains the following data.
You need to retrieve data to be used as context for a large language model (LLM). The solution must minimize token usage.
Which formal should you use to send the data to the LLM?
Answer: A
Explanation:
The correct choice is Option A because it provides the relevant semantic context the LLM needs while avoiding an unnecessary field that would add tokens without improving answer quality.
For LLM grounding and RAG-style context, Microsoft guidance emphasizes mapping and sending the fields that contain text pertinent to the use case . In this FAQ scenario, the useful context is the ProductName , the Question , and the Answer . Those three fields help the model understand both the subject domain and the actual Q & A pair. By contrast, FaqId is just a technical identifier and generally adds no semantic value for response generation, so including it wastes tokens.
That is why Option A is better than the others:
* Option A keeps the meaningful text fields and removes the low-value identifier.
* Option B is too minimal because it includes only the answer text as Prompt, which strips away the product and question context the LLM may need for accurate grounding.
* Option C keeps FaqId but omits ProductName, which can be important disambiguating context.
* Option D includes everything, but that does not minimize token usage because it keeps the unnecessary FaqId.
NEW QUESTION # 66
You have an Azure subscription. The subscription contains an Azure SQL database named SalesDB and an Azure App Service app named sales-api. sales-api uses virtual network integration to a subnet named vnet- prod/subnet-app and reads from SalesDB You need to configure authentication and network access to meet the following requirements:
* Ensure that sales-api connects to SalesDB by using passwordless authentication.
* Ensure that all the database traffic remains within the subscription.
The solution must minimize administrative effort
What should you configure? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
For authentication , the correct choice is Enable a managed identity and use Microsoft Entra authentication . Azure App Service supports managed identity, and Microsoft documents this as the recommended way to connect to Azure SQL Database without passwords or secrets . This minimizes administrative effort because there are no SQL passwords to rotate, store, or manage.
For network access , the correct choice is Create a private endpoint and disable public network access .
Microsoft documents that when public network access is disabled , Azure SQL Database only accepts connections through private endpoints . That keeps the database traffic on Azure private networking rather than exposing it through the public endpoint. Microsoft also notes that simply adding a private endpoint does not disable public access by itself, so disabling public network access is part of the correct configuration.
The other options are weaker:
* Rotated SQL credentials or storing SQL logins in Key Vault still use passwords and create more admin overhead.
* Allowing Azure services, adding outbound IP firewall rules, or database firewall rules still rely on the public endpoint , so they do not best satisfy the requirement that database traffic remain within the subscription.
NEW QUESTION # 67
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: A
Explanation:
To optimize your Microsoft SQL Server 2025 environment, you should transition from a brute- force distance calculation to an Approximate Nearest Neighbor (ANN) search using a DiskANN- based vector index.
The Solution: Vector Indexing
The VECTOR_DISTANCE function performs a full table scan, calculating the distance for every single row. During peak times, this consumes massive CPU cycles. A vector index allows SQL Server to navigate a pre-built graph to find matches, reducing complexity from O(n) to O(log n).
Implementation Steps
1. Create the Vector Index
You must specify the distance metric during index creation. For cosine similarity, use COSINE.
CREATE VECTOR INDEX idx_product_embedding
ON products (embedding)
WITH (METRIC = 'COSINE');
2. Update the Search Query
Replace the WHERE or ORDER BY clause that uses VECTOR_DISTANCE with the VECTOR_SEARCH function.
SELECT TOP (10) description, embedding
FROM products
WHERE VECTOR_SEARCH(
embedding,
@query_vector,
'COSINE',
10 -- Number of neighbors to return
);
Why This Solves Your Problem
CPU Relief: Avoids calculating distances for millions of rows.
Lower Latency: DiskANN is designed for sub-second retrieval even on large datasets.
Peak Performance: By offloading the math to a specialized index structure, the engine remains responsive for other transactional queries.
Reference:
https://learn.microsoft.com/en-us/azure/postgresql/extensions/how-to-use-pgdiskann
NEW QUESTION # 68
......
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