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| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Developing AI-Enabled Database Solutions |
| Exam Number: | DP-800 |
| Certificate Validity Period: | 1 year (renewable annually via free online assessment) |
| Available Languages: | Japanese, Chinese (Simplified), German, English, Portuguese (Brazil), Korean, French, Spanish |
| Real Exam Qty: | 40-60 |
| Exam Price: | $165 USD |
| Exam Duration: | 100 minutes |
| Passing Score: | 700 (on a scale of 1-1000) |
| Exam Format: | Case studies, Multiple select, Drag and drop, Multiple choice, Active screen |
| Recommended Training: | Microsoft Learn DP-800 Learning Path |
| Exam Registration: | Pearson VUE Registration |
| Sample Questions: | Microsoft DP-800 Sample Questions |
| Exam Way: | Online proctored or onsite at Pearson VUE test centers |
| Pre Condition: | No mandatory prerequisites; recommended experience: T-SQL development, SQL Server/Azure SQL, CI/CD practices, basic AI concepts |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/resources/study-guides/dp-800 |
PassTorrent provides proprietary preparation guides for the certification exam offered by the DP-800 exam dumps. In addition to containing numerous questions similar to the DP-800 Exam, the Developing AI-Enabled Database Solutions (DP-800) exam questions are a great way to prepare for the Microsoft DP-800 exam dumps.
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NEW QUESTION # 10
Drag and Drop Question
You have an Azure SQL database named SalesDB that supports an ecommerce application.
SalesDB contains a table named dbo.Orders that has a clustered index on a column named OrderId.
dbo.Orders receives continuous OLTP inserts and updates during business hours.
Your analytics team runs hourly aggregate queries that scan dbo.Orders to calculate revenue trends for recent dates.
You need to improve the performance of the hourly analytics queries without significantly affecting OLTP throughput. The solution must meet the following requirements:
- Support near-real-time (NRT) analytics on the dbo.Orders table.
- Reduce read time when retrieving analytical data from dbo.Orders.
- Support indexing only rows that match a predicate, such as Active =
1.
Which type of index should you use for each requirement? To answer, drag the appropriate index types to the correct requirements. Each index type 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:
Explanation:
NEW QUESTION # 11
You have a SQL database in Microsoft Fabric that uses the default settings for a newly created database and contains a table named Sales.Orders.
You have an application that uses two stored procedures to access Sales.Orders.
While monitoring database activity, you discover the following:
While monitoring database activity, you discover the following:
* sys.dm_exec_requests shows multiple sessions in a suspended state with wait_type = LCK_M_X. All the sessions show the same wait_resource, which maps to Sales.Orders, and the same nonzero blocking_session_id.
* sys.dm_exec_input_buffer(blocking_session_id, NULL) returns a last submitted command of BEGIN TRANSACTION UPDATE Sales.Orders.
* sys.dm_exec_sessions for the blocking session shows status = sleeping and open_transaction_count = 1.
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:
Explanation:
* The blocking is caused by an uncommitted explicit transaction in the blocking session that is holding locks. # Yes
* While an UPDATE operation on Sales.Orders is occurring, SELECT statements will be blocked. # No
* Joining sys.dm_tran_locks to sys.dm_exec_requests will show which session holds locks involved in the blocking chain. # Yes Comprehensive and Detailed Explanation with all Developing AI-Enabled Database Solutions documents : = The first statement is Yes . The evidence is conclusive: the blocking session is sleeping , has open_transaction_count = 1 , and its last submitted command was BEGIN TRANSACTION UPDATE Sales.
Orders . Microsoft specifically identifies a sleeping session with an open transaction as a classic blocking condition: modifications retain their exclusive locks until the transaction is committed or rolled back . The LCK_M_X waits and identical nonzero blocking_session_id values confirm that other requests are waiting for incompatible exclusive locks.
The second statement is No because READ_COMMITTED_SNAPSHOT is ON by default for SQL database in Microsoft Fabric . Under the default READ COMMITTED isolation level, reads therefore use row versioning rather than shared locks. Consequently, a normal SELECT can read the appropriate committed row version while an UPDATE holds locks, so the writer does not ordinarily block the reader. Microsoft explicitly documents RCSI as ON by default in SQL database in Microsoft Fabric .
The third statement is Yes in the context of blocking diagnostics. sys.dm_tran_locks exposes lock ownership through fields such as request_session_id , request_mode , and request_status , while sys.dm_exec_requests exposes active request and blocking_session_id information. Correlating these DMVs allows you to associate locks with sessions participating in a blocking chain. Microsoft specifically recommends sys.dm_tran_locks for determining which locks are currently held and sys.dm_exec_requests for identifying blocked and blocking sessions.
NEW QUESTION # 12
You have a database that contains production data. The schema is stored in a Git repository as an SDK-style SQL database project and contains the following reference data.
Name
Data type
RefID
int, identity
Code
nvarchar(10)
CreatedDate
datetime2
Description
nvarchar(255)
A deployment pipeline can be rerun automatically when a transient failure occurs.
You need to deploy the reference data as part of the same CI/CD process. Rerunning the pipeline must produce the same outcome and must NOT create duplicate rows.
What should you do?
Answer: B
NEW 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?
Answer: C
Explanation:
The correct workflow step is Option C because it uses the Azure SQL GitHub Action to publish a .dacpac file and reads the connection string from GitHub repository secrets , which is exactly what the requirement asks for. Microsoft's Azure SQL GitHub Actions guidance shows using azure/sql-action@v2 with a connection string stored in secrets and a DACPAC path for deployment.
The key parts that make C correct are:
* uses: azure/sql-action@v2
* action: publish
* path: bin/Debug/db1.dacpac
* connection-string: ${{ secrets.SQL_CONNECTION_STRING }}
That matches the documented publish pattern for deploying a DACPAC to Azure SQL Database from GitHub Actions. Microsoft and the Azure SQL action documentation both describe Publish as the deployment action for applying a DACPAC to a target database, while Extract is used to create a DACPAC from an existing database, not deploy one.
Why the other options are incorrect:
* A uses an environment variable defined inline with a visible connection string rather than using GitHub repository secrets , which does not meet the requirement.
* B uses action: extract, which would create a DACPAC from a database instead of publishing the existing DACPAC artifact.
* D passes a target connection string to dotnet build, but the question says the workflow already runs dotnet build and produces a database artifact . The missing step is the deployment/publish step, not another build step. Microsoft's SQL project automation guidance separates build the DACPAC from publish the DACPAC .
NEW QUESTION # 14
Your development team uses GitHub Copilot Chat in Microsoft SQL Server Management Studio (SSMS) to generate and run Transact-SQL queries against an Azure SQL database named DB1 DB1 contains tables that store sensitive customer data.
You need to ensure that any Transact SQL queries that run from GitHub Copilot Chat In SSMS are restricted by the same permissions as the developer ' s database login.
What prevents the GitHub Copilot Chat-run queries from accessing data beyond the developer ' s access?
Answer: D
Explanation:
The correct answer is B . Microsoft's SSMS Copilot documentation states that queries from Copilot in SSMS are executed under the context of the user's login and permissions , and that there are no separate permissions for Copilot in SSMS . That means Copilot-run Transact-SQL cannot access more data than the developer's own database principal is already allowed to access.
That is why the other options are incorrect:
* A is wrong because Copilot does not use a separate read-only sandbox in place of database permissions.
* C is wrong because enforcement is not a client-side filtering trick; it is enforced by the database security context of the current login.
* D is wrong because Copilot does not apply a different RLS model from the developer; it simply runs under the same login context.
So the security boundary is the developer's existing database identity and permissions .
NEW QUESTION # 15
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