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Our objective is to make Microsoft DP-800 test preparation process of every aspirant smooth. Therefore, we have introduced three formats of our Developing AI-Enabled Database Solutions DP-800 Exam Questions. To ensure the best quality of each format, we have tapped the services of experts. They thoroughly analyze Developing AI-Enabled Database Solutions DP-800 Exam’s content, Microsoft DP-800 past tests, and add the DP-800 real exam questions in our three formats.
NEW QUESTION # 12
Which service enables natural language querying over SQL data?
Answer: D
Explanation:
Azure OpenAI Service allows users to query structured SQL data using natural language via LLMs.
NEW QUESTION # 13
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 need to create a solution that meets the development requirements for retrieving the patient lists.
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 # 14
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?
Answer: A
NEW QUESTION # 15
You have an Azure SQL database that contains a table named Tickets_Embeddings. Tickets_Embeddings contains a VECTOR(1536) column.
Embeddings are generated by using text-embedding-ada-002 and the /openai/v1/embeddings endpoint.
After a review of the current environment, the following changes are requested:
The AI architect has recommended switching to text-embedding-3-small.
The security team requires authentication by using Microsoft Entra managed identities only. Storing API keys in an application is prohibited.
Which two actions should you include in the solution? Each correct answer presents a part of the solution.
NOTE: Each correct selection is worth one point.
Answer: A,D
NEW QUESTION # 16
You have an Azure SQL database that supports an AI-driven product search API.
You need to identify the top CPU-consuming queries from the last two hours by using Query Store data. The solution must aggregate CPU consumption across executions and return only the top 15 query hashes.
How should you complete the Transact-SQL code? To answer, drag the appropriate values to the correct targets. Each value may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Verified Answer : =
* CPU aggregation expression # rs.avg_cpu_time
* Runtime interval source # sys.query_store_runtime_stats_interval
* Last two hours filter # DATEADD(HOUR, -2, GETUTCDATE())
Comprehensive and Detailed Explanation with all Developing AI-Enabled Database Solutions documents : = The first correct selection is rs.avg_cpu_time . Query Store stores CPU statistics per aggregation interval, and Microsoft documents avg_cpu_time as the average CPU time per execution, in microseconds . To calculate total CPU consumption across all executions, multiply count_executions by avg_cpu_time , then divide by
1000.0 to convert microseconds to milliseconds:
SUM(count_executions * rs.avg_cpu_time / 1000.0)
This is the exact pattern Microsoft uses in its documented query for identifying the top 15 CPU-consuming queries by query hash .
The second selection is sys.query_store_runtime_stats_interval because sys.query_store_runtime_stats.
runtime_stats_interval_id is a foreign key to this view. The interval view provides start_time and end_time , allowing Query Store data to be limited to the required time window.
The third selection is DATEADD(HOUR, -2, GETUTCDATE()) . Microsoft's own Azure SQL performance- monitoring example for the last two hours uses exactly:
rsi.start_time > = DATEADD(HOUR, -2, GETUTCDATE())
and then ranks by total CPU descending to return the top 15 query hashes.
Final drag-and-drop selections:
* First target: rs.avg_cpu_time
* Second target: sys.query_store_runtime_stats_interval
* Third target: DATEADD(HOUR, -2, GETUTCDATE())
NEW QUESTION # 17
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