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28. Frage
You have an Azure SQL database named SalesDB on a logical server named sales-sql01.
You have an Azure App Service web app named OrderApi that connects to SalesDB by using SQL authentication.
You enable a user-assigned managed identity named OrderApi-Id for OrderApi.
You need to configure OrderApi to connect to SalesDB by using Microsoft Entra authentication.
The managed identity must have read and write permissions to SalesDB.
Which Transact-SQL statements should you run in SalesDB?
Antwort: C
Begründung:
Create the Database User and Grant Permissions
You must use Transact-SQL (T-SQL) to create a "contained database user" that represents the managed identity and assign it to the appropriate database roles for read/write access.
Connect to the database using an account with Entra Admin privileges (e.g., via Azure Data Studio or the Azure portal's Query editor).
Execute the following T-SQL commands in the context of the specific database (not the master database):
-- Create a user for the User-Assigned Managed Identity
-- Replace with the exact name of your user-assigned managed identity
CREATE USER [] FROM EXTERNAL PROVIDER;
-- Add the user to the db_datareader role (Read permissions)
ALTER ROLE db_datareader ADD MEMBER [];
-- Add the user to the db_datawriter role (Write permissions)
ALTER ROLE db_datawriter ADD MEMBER [];
Reference:
https://medium.com/@yuvchauhan15/managing-access-in-azure-sql-granting-read-and-write- privileges-to-a-managed-identity-8dacc4d205fd
29. Frage
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.
You need to enable similarity search to provide the analysts with the ability to retrieve the most relevant health summary reports. The solution must minimize latency. What should you include in the solution?
Antwort: B
Begründung:
Scenario: There is a VehicleHealthSummary table.
To enable similarity search on your health summary data while minimizing latency, you should use the native VECTOR data type and a DiskANN vector index, which are now available in public preview for Azure SQL Database.
Solution Implementation
1. Define the Vector Column: Ensure your embeddings are stored using the native VECTOR(1536) type rather than NVARCHAR or VARBINARY. This format is optimized for high- dimensional data and mathematical operations.
ALTER TABLE HealthSummaries
ADD SummaryVector VECTOR(1536);
2. Create the Vector Index: Use the CREATE VECTOR INDEX statement. In Azure SQL, this uses the DiskANN algorithm, which is specifically designed to provide high-speed Approximate Nearest Neighbor (ANN) searches for large datasets.
CREATE VECTOR INDEX idx_health_summary_vector
ON HealthSummaries (SummaryVector)
WITH ( METRIC = 'COSINE', TYPE = 'DISKANN' );
3. Perform the Similarity Search: To leverage the index for low-latency retrieval, use the VECTOR_SEARCH function rather than VECTOR_DISTANCE. While VECTOR_DISTANCE calculates exact values (resulting in a full table scan), VECTOR_SEARCH utilizes the DiskANN index to find the most relevant reports quickly.
SELECT TOP(10) *
FROM HealthSummaries
ORDER BY VECTOR_DISTANCE('cosine', SummaryVector, @query_vector);
Reference:
https://learn.microsoft.com/en-us/samples/azure-samples/azure-sql-db-openai/azure-sql-db- openai/
30. Frage
You have a SQL database in Microsoft Fabric named SalesDB that contains a table named dbo.Products.
You need to modify SalesDB to meet the following requirements:
Create a vector index on the appropriate column.
Use a supplied natural language query vector.
How should you complete the Transact-SQL code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Antwort:
Begründung:
Explanation:
Verified Answer : =
* Vector index column # embedding
* Vector search function # VECTOR_SEARCH
* Final clause # ORDER BY s.distance
The first selection is embedding because CREATE VECTOR INDEX must target the column that stores vector data. Microsoft documents the syntax as CREATE VECTOR INDEX ... ON object (vector_column) , so the index belongs on dbo.Products.embedding , not on distance or product_name .
The second selection is VECTOR_SEARCH . This function is designed for vector similarity retrieval and works with vector indexes in SQL database in Microsoft Fabric . In the shown code, the supplied
@query_vector VECTOR(1536) is compared against the embedding column using cosine distance, and TOP_N = 10 requests the ten nearest matches. Microsoft specifically documents VECTOR_SEARCH for approximate nearest-neighbor searches and its integration with vector indexes.
The final clause is ORDER BY s.distance because VECTOR_SEARCH returns a distance value representing how far each stored vector is from the supplied query vector. With cosine distance, smaller distance means greater similarity , so ascending order returns the most relevant products first.
Therefore, the completed selections are:
CREATE VECTOR INDEX idx_products_embedding
ON dbo.Products (embedding)
WITH (METRIC = ' cosine ' , TYPE = ' DiskANN ' );
FROM VECTOR_SEARCH (
TABLE = dbo.Products AS t,
COLUMN = embedding,
SIMILAR_TO = @query_vector,
METRIC = ' cosine ' ,
TOP_N = 10
) AS s
ORDER BY s.distance;
So the verified hotspot answers are embedding # VECTOR_SEARCH # ORDER BY s.distance .
31. Frage
You have an Azure SQL database named ProductsDB.
You deploy Data API builder (DAB) to Azure Container Apps by using the
mcr.microsoft.com/azure-databases /data-api-builder:latest image.
The container app has the following configurations:
- Secrets: mssql-connection-string, dab-config-base64
- Environment variables:
- MSSQL_CONNECTION_STRING=secretref:mssql-connection-string
- DAB_CONFIG_BASE64=secretref:dab-config-base64
- Ingress: External on port 5000
Users report that the /health endpoint returns a healthy response, but all requests that query an entity named Products fail and generate a connection error.
You confirm that the SQL login in the connection string is correct and the database exists.
You need to ensure that the container app can establish connections to the Azure SQL logical server without changing the container app deployment settings or the DAB configuration file.
What should you do on the Azure SQL logical server?
Antwort: B
Begründung:
Even if the login credentials are correct, Azure SQL Database blocks all incoming traffic by default. Since your Container App is returning a healthy response for the /health endpoint (which is internal to the DAB engine) but failing on entity queries (which require a database hit), the network handshake is being rejected at the SQL Server firewall level.
To fix this connection error without changing the container app or the DAB configuration file, you must enable the Azure SQL Server firewall to allow Azure services.
Setting the start and end IP address to 0.0.0.0 in an Azure SQL Database firewall rule is a specific configuration that enables the "Allow Azure services and resources to access this server" setting.
Primary Use Case
In your scenario, this rule allows your Azure Container Apps (ACA) to communicate with your Azure SQL Database over the Azure backbone network.
Connectivity: It permits any traffic originating from within the Azure boundary to reach the database.
Simplicity: You do not need to track or white-list the specific outbound IP addresses of your Container App, which can change if the app scales or restarts.
Internal Routing: Traffic stays within the Azure network rather than routing out to the public internet and back in.
Reference:
https://learn.microsoft.com/en-us/azure/azure-sql/database/firewall-configure
https://stackoverflow.com/questions/54599813/how-to-enable-the-access-to-azure-services-in- my-azure-sql-database-server
32. Frage
You need to recommend a solution for the development team to retrieve the live metadata. The solution must meet the development requirements.
What should you include in the recommendation?
Antwort: B
33. Frage
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