Developing AI-Enabled Database Solutions v1.0
Question 1
Case Study -
This is a case study. Case studies are not timed separately. You can use as much exam time as you would like to complete each case. However, there may be additional case studies and sections on this exam. You must manage your time to ensure that you are able to complete all questions included on this exam in the time provided.
To answer the questions included in a case study, you will need to reference information that is provided in the case study. Case studies might contain exhibits and other resources that provide more information about the scenario that is described in the case study. Each question is independent of the other questions in this case study.
At the end of this case study, a review screen will appear. This screen allows you to review your answers and to make changes before you move to the next section of the exam. After you begin a new section, you cannot return to this section.
To start the case study -
To display the first question in this case study, click the Next button. Use the buttons in the left pane to explore the content of the case study before you answer the questions. Clicking these buttons displays information such as business requirements, existing environment and problem statements. If the case study has an All Information tab, note that the information displayed is identical to the information displayed on the subsequent tabs. When you are ready to answer a question, click the Question button to return to the question.
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 FeedbackJson column has a full-text index and stores JSON documents in the following format.
The support staff at Contoso never has the UNMASK permission.
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.
VehicleIncidentReports often 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 accuracy JSON 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 MaintenanceEvents table updates the corresponding value in the LastModifiedUtc column 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 FeedbackJson column of the CustomerFeedback table:
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 recommend a solution that will resolve the ingestion pipeline failure issues.
Which two actions should you recommend? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
- A. Enable snapshot isolation on the database.
- B. Use a trigger to automatically rewrite malformed JSON.
- C. Add foreign key constraints on the table.
- D. Create a unique index on a hash of the payload.
- E. Add a check constraint that validates the JSON structure.
Answer : DE
Question 2
You have a Microsoft SQL Server 2025 instance that has a managed identity enabled.
You have a 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 solution must provide the highest level of security.
You write the following Transact-SQL code.
What should you insert at line 02?
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A.
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B.
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C.
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D.
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E.
Answer : B
Question 3
You have an Azure SQL database named SalesDB that contains a table named dbo.Articles. dbo.Articles contains two million articles with embeddings. The articles are updated frequently throughout the day.
You query the embeddings by using VECTOR_SEARCH.
Users report that semantic search results do NOT reflect the updates until the following day.
You need to ensure that the embeddings are updated whenever the articles change. The solution must minimize CPU usage on SalesDB.
Which embedding maintenance method should you implement?
- A. Modify the query to use VECTOR_DISTANCE instead of VECTOR_SEARCH.
- B. Enable change data capture (CDC) on dbo.Articles and use an Azure Functions app to process CDC changes.
- C. Run an hourly Transact-SQL job that regenerates embeddings for all the rows in dbo.Articles.
- D. On dbo.Articles, create a trigger that calls AI_GENERATE_EMBEDDINGS for each inserted or updated row.
Answer : B
Question 4
You have a GitHub Codespaces environment that has GitHub Copilot Chat installed and is connected to a SQL database in Microsoft Fabric named DB1. DB1 contains tables named Sales.Orders and Sales.Customers.
You use GitHub Copilot Chat in the context of DB1.
A company policy prohibits sharing customer Personally Identifiable Information (PII), secrets, and query result sets with any AI service.
You need to use GitHub Copilot Chat to write and review Transact-SQL code for a new stored procedure that will join Sales.Orders to Sales.Customers and return customer names and email addresses. The solution must NOT share the actual data in the tables with GitHub Copilot Chat.
What should you do?
- A. From Sales.Customers, paste several rows that include email addresses into a chat, so that GitHub Copilot Chat can infer edge cases.
- B. Run a SELECT statement that returns customer names and email addresses and provide the result set to GitHub Copilot Chat so that GitHub Copilot Chat can generate the stored procedure.
- C. Provide the database connection string to GitHub Copilot Chat so that GitHub Copilot Chat can validate the stored procedure.
- D. Ask GitHub Copilot Chat to generate the stored procedure by using schema details only.
Answer : D
Question 5
HOTSPOT -
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 : 
Question 6
DRAG DROP -
You have a database named DB1. The schema is stored in a GitHub repository as an SDK-style SQL database project.
You use a feature branch workflow to deploy changes to DB1.
You need to update the local feature branch with the latest changes to main, and then create a pull request to merge the feature branch into main for review.
How should you complete the GitHub CLI script? To answer, drag the appropriate values to the correct targets. Each value 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 : 
Question 7
You have an Azure SQL database that contains tables named dbo.ProductDocs and dbo.ProductDocsEmbeddings. dbo.ProductDocs contains product documentation and the following columns:
DocID (int)
Title (nvarchar(200))
Body (nvarchar(max))
LastModified (datetime2)
The documentation is edited throughout the day.
dbo.ProductDocsEmbeddings contains the following columns:
DocID (int)
ChunkOrder (int)
ChunkText (nvarchar(max))
Embedding (vector(1536))
The current embedding pipeline runs once per night.
You need to ensure that embeddings are updated every time the underlying documentation content changes. The solution must NOT require a nightly batch process.
What should you include in the solution?
- A. fixed-size chunking
- B. a smaller embedding model
- C. table triggers
- D. change tracking on dbo.ProductDocs
Answer : D
Question 8
HOTSPOT -
You have a database named db1. The schema is stored in a Git repository as an SDK-style SQL database project. The repository contains the following GitHub Action workflow.
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 : 
Question 9
You have a Microsoft SQL Server 2025 instance that contains a database named SalesDB. SalesDB supports a Retrieval Augmented Generation (RAG) pattern for internal support tickets. The SQL Server instance runs without any outbound network connectivity.
You plan to generate embeddings inside the SQL Server instance and store them in a table for vector similarity queries.
You need to ensure that only a database user account named AIApplicationUser can run embedding generation by using the model.
Which two actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
- A. Grant the CONTROL permission on SalesDB to AIApplicationUser.
- B. Create a database audit specification on SalesDB owned by AIApplicationUser.
- C. Grant the EXECUTE permission on the external model project to AIApplicationUser.
- D. Create an external model project by using ONNX runtime and local paths.
- E. Create an external model project that points to a Microsoft Foundry REST endpoint.
Answer : CE
Question 10
DRAG DROP -
You have an Azure SQL database that supports an OLTP application.
You need to write Transact-SQL code that returns blocking chain details. The output must return only sessions that are blocked or are blocking other sessions.
How should you complete the code? To answer, drag the appropriate values to the correct targets. Each value 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 : 
Question 11
You have an Azure SQL database that supports a customer-facing API. The API calls a stored procedure named dbo.GetCustomerOrders thousands of times per hour.
After a deployment that updated indexes and statistics, users report that the API endpoint backed by dbo.GetCustomerOrders is slower. In Query Store, the same query now has two persisted execution plans. During the last hour, the newer plan had a significantly higher average duration and CPU time than the older plan.
You need to restore the previous performance quickly, without changing the API code.
Which Transact-SQL command should you run?
- A. EXEC sys.sp_query_store_set_hints
- B. DBCC FREEPROCCACHE
- C. EXEC sp_query_store_force_plan
- D. ALTER DATABASE
Answer : C
Question 12
HOTSPOT -
You have an Azure SQL database that has Query Store enabled.
Query Performance Insight shows that one stored procedure has the longest runtime. The procedure runs the following parameterized query.
The dbo.Orders table has approximately 120 million rows. CustomerId is highly selective, and OrderDate is used for range filtering and sorting.
You have the following indexes:
Clustered index: PK_Orders on (OrderId)
Nonclustered index: IX_Orders_OrderDate on (OrderDate) with no included columns
An actual execution plan captured from Query Store for slow runs shows the following:
An index seek on IX_Orders_OrderDate followed by a Key Lookup (Clustered) on PK_Orders for CustomerId, Status, and TotalAmount
A sort operator before Top (50), because the results are ordered by OrderDate DESC
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 : 
Question 13
You have a database named DB1. The schema is stored in a Git repository as an SDK-style SQL database project.
You have a GitHub Actions workflow that already runs dotnet build and produces a database artifact.
You need to add a deployment step that publishes the .dacpac file to an Azure SQL database by using the secrets stored in GitHub repository secrets.
What should you include in the workflow?
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A.
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B.
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C.
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D.
Answer : C
Question 14
HOTSPOT -
You have a Microsoft Fabric workspace named Workspace1 that contains a SQL database named SalesDB and an API for GraphQL item 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 : 
Question 15
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?
- A. A long-running SELECT statement is blocking writers.
- B. Session 72 caused a deadlock.
- C. An explicit transaction was started but not committed or rolled back.
- D. A lock escalation occurred.
Answer : C