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Microsoft Developing AI-Enabled Database Solutions Sample Questions (Q16-Q21):

NEW QUESTION # 16
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 use the UsefulPrompts table as defined in the AI requirements. Which stored procedure should you use?

Answer: A

Explanation:
Scenario:
The UsefulPrompts table will store prompts that doctors can use to help diagnose patient illness by connecting to an Azure OpenAI endpoint.
The system stored procedure sp_invoke_external_rest_endpoint is used to connect an Azure SQL Database to an Azure OpenAI endpoint.This procedure allows you to call HTTPS REST endpoints directly from your database, enabling the integration of generative AI or embedding models into your SQL workflows without an intermediate application layer.
Reference:
https://blog.fabric.microsoft.com/en-gb/blog/ai-ready-apps-from-rag-to-chat-interacting-with-sql- database-in-microsoft-fabric-using-graphql-and-mcp


NEW QUESTION # 17
Hotspot Question
You have a SQL database in Microsoft Fabric that contains a table named dbo.Products.
dbo.Products contains product catalog data.
You need to create a stored procedure that performs hybrid search. The solution must meet the following requirements:
- Use approximate nearest neighbor (ANN) to retrieve the top 20
candidate products.
- Re-rank only the candidates that also match a full-text query.
- Generate the query embedding.
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.

Answer:

Explanation:


NEW QUESTION # 18
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 plan to implement changes to sp_UpdateProcedureForPatient to meet the performance requirements.
You change the stored procedure to run all its code within a transaction.
Which transaction level should you use?

Answer: C

Explanation:
Scenario: Database Performance Requirements
Records accessed by using sp_UpdateProcedureForPatient must NOT be changed by other transactions while the stored procedure runs.
To prevent other transactions from changing records while your stored procedure runs, you should use the Repeatable Read isolation level.
Repeatable ReadMechanism: Places shared locks on all data read by the query.
Prevention: Prevents other transactions from modifying or deleting the rows you have accessed.
Consistency: Ensures that if you read the same data twice within the procedure, the values remain identical.
Incorrect:
[Not C]
Serializable
Strictness: This is the highest level of isolation.
Phantom Protection: In addition to preventing changes to existing records, it prevents other transactions from inserting new records that would fall into the range of your query (phantom reads).
Usage: Use this if your procedure relies on aggregate counts or specific ranges that must remain absolutely static.
Key Considerations
Concurrency: Both levels reduce system performance because they hold locks longer, potentially causing other users to wait.
Deadlocks: Higher isolation levels increase the risk of deadlocks in busy databases.
Reference:
https://learn.microsoft.com/en-us/sql/connect/jdbc/understanding-isolation-levels


NEW QUESTION # 19
What is the benefit of using embeddings over keyword search?

Answer: D

Explanation:
Embeddings capture semantic meaning, enabling context-based retrieval.


NEW QUESTION # 20
You have an Azure SQL database that contains a table named dbo.Products. dbo.Products contains three columns named Embedding, Category, and Price. The Embedding column is defined as VECTOR(1536).
You use AI_GENERATE_EMBEDDINGS and VECTOR_SEARCH to support semantic search and apply additional filters on two columns named Category and Price.
You plan to change the embedding model from text-embedding-ada-002 to text-embedding-3- small. Existing rows already contain embeddings in the Embedding column.
You need to implement the model change. Applications must be able to use VECTOR_SEARCH without runtime errors.
What should you do first?

Answer: D

Explanation:
To ensure your applications can transition models without runtime errors while using VECTOR_SEARCH, you must first define a Vector Index that explicitly identifies the dimensions and distance metric.
Since you are moving from text-embedding-ada-002 to text-embedding-3-small, both models default to 1536 dimensions, which matches your existing column definition. To create the index as the first step, use the following SQL:
CREATE VECTOR INDEX idx_embedding ON YourTableName (Embedding)
WITH ( DISTANCE_METRIC = 'COSINE' );
Use code with caution.
Why this works:
Schema Consistency: Because both models use 1536 dimensions, you don't need to alter the VECTOR(1536) column type immediately.
Search Stability: Creating the index allows the engine to optimize the VECTOR_SEARCH function. As long as the incoming query vector (generated by the app) matches the dimensions of the stored vectors, the search will execute without a runtime dimension mismatch error.
Reference:
https://docs.couchbase.com/cloud/n1ql/n1ql-language-reference/vectorfun.html


NEW QUESTION # 21
......

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