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| Section | Objectives |
|---|---|
| Design and implement data solutions | - Implement data storage and data processing solutions - Design database solutions using Azure data services |
| Monitor, troubleshoot, and maintain solutions | - Troubleshooting data pipeline issues - Monitoring database health and performance |
| Integrate AI capabilities with database systems | - Use Azure AI services with database workloads - Implement AI-assisted data processing |
| Develop and manage database solutions | - Optimize performance and scalability - Ensure security and compliance of data solutions |
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NEW QUESTION # 74
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?
Answer: C
Explanation:
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/
NEW QUESTION # 75
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: C
NEW QUESTION # 76
You have an Azure SQL database that contains a table named Rooms. Roomswas created by using the following Transact-SQL statement.
You discover that some records in the Rooms table contain NULL values for the Owner field.
You need to ensure that all future records have a value for the Owner field.
What should you add?
Answer: C
Explanation:
A CHECK constraint is one way to do it.
If you use a CHECK constraint (e.g., CHECK (ColumnName IS NOT NULL)), the database will indeed reject new NULL entries. However, the column's metadata will still technically allow NULLs, which can sometimes affect how external tools or APIs interact with your schema.
Reference:
https://www.postgresql.org/docs/7.0/sql-createtable.htm
NEW QUESTION # 77
Case Study 2 - Fabrikam
Existing Environment
Azure Environment
Fabrikam has a single Azure subscription in the East US 2 Azure region. The subscription contains an Azure SQL database named DB1. DB1 contains the following tables:
* Patients
* Employees
* Procedures
* Transactions
* UsefulPrompts
* ProcedureDocuments
You store a column master key as a secret in Azure Key Vault.
You have an on-premises application named TransactionProcessing that uses a hard-coded username and password in a connection string to access DB1.
Problem Statements
Users report that after executing a long-running stored procedure named sp_UpdateProcedureForPatient, updates to the underlying data are sometimes inconsistent.
Requirements
Planned Changes
Fabrikam plans to manage all changes to Azure SQL Database objects by using source control in GitHub. Every pull request submitted to production will be validated before it can be merged.
Deployments must use the Release configuration.
Security Requirements
Fabrikam identifies the following security requirements:
* The TransactionProcessing application must use a passwordless connection to DB1.
* The Employees table contains two columns named TaxID and Salary that must be encrypted at rest.
* Auditors must have a tamper-evident history of transactions with cryptographic proof of changes to the employee data.
Database Performance Requirements
Records accessed by using sp_UpdateProcedureForPatient must NOT be changed by other transactions while the stored procedure runs.
AI Search, Embeddings, and Vector Indexing
Fabrikam identifies the following AI-related requirements:
* Queries to the ProcedureDocuments table must use Reciprocal Rank Fusion (RRF).
* Users must be able to query the data in DB1 by using prompts in Copilot in Microsoft Fabric.
* The UsefulPrompts table will store prompts that doctors can use to help diagnose patient illness by connecting to an Azure OpenAI endpoint.
Development Requirements
Fabrikam identifies the following development requirements:
* Provide the functionality to retrieve all the transactions of a given patient between two dates, showing a running total.
* Expose a Data API builder (DAB) configuration file to enable Azure services to perform the following operations over a REST API:
- Read data from the procedures table without authentication.
- Read and insert data into the Transactions table once authenticated.
- Execute the sp_UpdateProcedurePatient stored procedure.
* Provide the functionality to retrieve a list of the names of patients who underwent medical procedures during the last 30 days.
* Information for each medical procedure will be stored in a table. The table will be used with a large language model (LLM) for user querying and will have the following structure.
DAB
You create a DAB configuration file that meets the development requirements for DB1 and includes the following entities.
You need to recommend a solution for the TransactionProcessing application that meets the security requirements. What should you include in the recommendation?
Answer: D
Explanation:
Scenario:
You have an on-premises application named TransactionProcessing that uses a hard-coded username and password in a connection string to access DB1.
Security Requirements: Fabrikam identifies the following security requirements: The TransactionProcessing application must use a passwordless connection to DB1.
The most appropriate security solution for an on-premises application connecting to an Azure SQL Database without passwords is Microsoft Entra ID authentication. Specifically, you should implement one of the following Entra-based mechanisms depending on your application's environment and architecture:
*-> 1. Microsoft Entra Service Principal with Certificate
This is the standard "passwordless" approach for non-Azure (on-premises) applications.
How it works: You register the application in Microsoft Entra ID. Instead of a client secret (password), you use a certificate for authentication.
Benefit: No secrets are stored in your code or configuration files. The application uses the certificate to obtain an access token from Entra ID to connect to the database.
2. Microsoft Entra Integrated Authentication
3. Managed Identity (via Azure Arc)
For a more modern approach, you can register your on-premises server with Azure Arc.
How it works: Azure Arc extends Azure's Managed Identity capabilities to on-premises hardware.
Benefit: This is the "gold standard" for passwordless security, as Azure handles the identity, rotation, and lifecycle of the credentials automatically.
Reference:
https://learn.microsoft.com/en-us/azure/developer/java/spring-framework/migrate-sql-database- to-passwordless-connection
NEW QUESTION # 78
You have an Azure SQL database.
You need to create a scalar user-defined function (UDF) that returns the number of whole years between an input parameter named 0orderDate and the current date/time as a single positive integer. The function must be created in Azure SQL Database. You write the following code.
What should you insert at line 05?
Answer: D
Explanation:
The correct answer is D because the scalar UDF must return the number of whole years from the input
@OrderDate to the current date/time as a single positive integer . The correct DATEDIFF order is:
DATEDIFF(year, @OrderDate, GETDATE())
Microsoft documents that DATEDIFF(datepart, startdate, enddate) returns the count of specified datepart boundaries crossed between the start and end values. Since @OrderDate is the earlier date and GETDATE() is the later date, this ordering returns a positive result for past order dates.
The other choices are incorrect:
* A reverses the arguments and would return a negative value for a past order date.
* B is missing RETURN, and converting month difference to years by dividing by 12 is not the direct whole-year expression the question asks for.
* C subtracts year parts only, which can be off around anniversary boundaries because it ignores whether the full year has actually elapsed.
So the correct insertion at line 05 is:
RETURN DATEDIFF(year, @OrderDate, GETDATE());
NEW QUESTION # 79
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