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NEW QUESTION # 17
Which data type is used to store vector embeddings in SQL?
Answer: C
Explanation:
Embeddings are stored as arrays or binary/vector types depending on SQL engine support.
NEW QUESTION # 18
You have an Azure SQL database that contains a table named dbo.SupportTickets. dbo.SupportTickets contains a JSON column named Payload and a datetime column CreatedAt.
You need to generate a report for the last seven days that meets the following requirements:
* Returns exactly one row per customer per day
* For each customer and day, returns the earliest ticket
* Includes the customer ID stored in Payload
How should you complete the Transact-SQL query? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
CustomerId extraction # JSON_VALUE
Ranking function # ROW_NUMBER
Final filter # 1
The first selection is JSON_VALUE because CustomerId is stored as a scalar property in the JSON payload at
$.customer.id. Microsoft documents that JSON_VALUE extracts a scalar value from JSON text, whereas OPENJSON is used to shred JSON into relational rows/columns.
The second selection is ROW_NUMBER. The query partitions the data by:
CAST(t.CreatedAt AS date),
JSON_VALUE(t.Payload, ' $.customer.id ' )
and orders each partition by:
t.CreatedAt ASC
Microsoft documents that ROW_NUMBER() assigns sequential numbers starting at 1 within each partition.
Therefore, the earliest ticket for each customer on each day receives rn = 1.
The third selection is therefore 1, because filtering with:
WHERE rn = 1
returns exactly one row per customer per calendar day-the earliest ticket in that partition.
So the completed logic is:
WITH TicketRanks AS
(
SELECT
t.TicketId,
CAST(t.CreatedAt AS date) AS TicketDate,
JSON_VALUE(t.Payload, ' $.customer.id ' ) AS CustomerId,
t.CreatedAt,
ROW_NUMBER() OVER
(
PARTITION BY
CAST(t.CreatedAt AS date),
JSON_VALUE(t.Payload, ' $.customer.id ' )
ORDER BY t.CreatedAt ASC
) AS rn
FROM dbo.SupportTickets AS t
WHERE t.CreatedAt > = DATEADD(day, -7, SYSUTCDATETIME())
)
SELECT
TicketDate,
CustomerId,
TicketId,
CreatedAt
FROM TicketRanks
WHERE rn = 1
ORDER BY TicketDate, CustomerId;
Final hotspot selections:
First dropdown: JSON_VALUE
Second dropdown: ROW_NUMBER
Third dropdown: 1
NEW QUESTION # 19
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: A
NEW QUESTION # 20
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.
Answer: A,B
Explanation:
To implement a Retrieval Augmented Generation (RAG) pattern in an isolated SQL Server 2025 instance, you can use the new native vector capabilities to generate, store, and query embeddings without needing outbound internet access.
[E] 1. Enable External REST Endpoints
Because your instance is isolated, you must first enable the configuration that allows SQL Server to communicate with your internal Microsoft Foundry (or local) endpoint.
EXEC sp_configure 'external rest endpoint enabled', 1;
RECONFIGURE;
2. Create the External Model Project
Register your Microsoft Foundry REST endpoint as an external model. This allows SQL Server to treat the internal service as a registered provider for generating embeddings.
[C] 3. Grant Permission to a Specific User
To restrict embedding generation to a single specific database user, grant the EXECUTE permission on the newly created external model.
-- Granting EXECUTE only to the specific user 'AppUser'
GRANT EXECUTE ON EXTERNAL MODEL::[MyFoundryEmbeddingModel] TO [AppUser]; Use code with caution.
4. Generate and Store Embeddings
Use the AI_GENERATE_EMBEDDINGS function to process text into vectors and store them in a table with the new VECTOR data type.
Reference:
https://www.red-gate.com/simple-talk/databases/sql-server/sql-server-2025-create-external- model-and-ai_generate_embeddings-commands-explained/
NEW QUESTION # 21
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.
Hotspot Question
You are evaluating the DAB configuration.
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:
Explanation:
NEW QUESTION # 22
......
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