Microsoft DP-800 Testking Learning Materials - DP-800 Study Group

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Microsoft DP-800 Exam Syllabus Topics:

TopicDetails
Topic 1
  • Design and develop database solutions: This domain covers designing and building database objects such as tables, views, functions, stored procedures, and triggers, along with writing advanced T-SQL code and leveraging AI-assisted tools like GitHub Copilot and MCP for SQL development.
Topic 2
  • Implement AI capabilities in database solutions: This domain covers designing and managing external AI models and embeddings, implementing full-text, semantic vector, and hybrid search strategies, and building retrieval-augmented generation (RAG) solutions that connect database outputs with language models.
Topic 3
  • Secure, optimize, and deploy database solutions: This domain focuses on implementing data security measures like encryption, masking, and row-level security, optimizing query performance, managing CI
  • CD pipelines using SQL Database Projects, and integrating SQL solutions with Azure services including Data API builder and monitoring tools.

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Microsoft Developing AI-Enabled Database Solutions Sample Questions (Q55-Q60):

NEW QUESTION # 55
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 AlApplicationUser 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,C

Explanation:
Because the SQL Server 2025 instance has no outbound network connectivity , the embedding model cannot rely on a remote REST endpoint such as Azure AI Foundry or Azure OpenAI. Microsoft's CREATE EXTERNAL MODEL documentation includes a local deployment pattern using ONNX Runtime running locally with local runtime/model paths . That is the right design when embeddings must be generated inside the SQL Server instance without external network access. Microsoft explicitly documents a local ONNX Runtime example for SQL Server 2025 and notes the required local runtime setup and model path configuration.
The permission requirement is handled by granting the application user access to use the external embeddings model. Microsoft's AI_GENERATE_EMBEDDINGS documentation states that, as a prerequisite, you must create an external model of type EMBEDDINGS that is accessible via the correct grants, roles, and/or permissions . Among the choices, the exam-appropriate action is to grant execute permission on the external model project to AlApplicationUser so only that database user can run embedding generation through the model.


NEW QUESTION # 56
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: B


NEW QUESTION # 57
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 (Pll), secrets, and query result sets with any Al 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?

Answer: A

Explanation:
The correct answer is D because the policy explicitly prohibits sharing customer PII, secrets, and query result sets with any AI service. The safe way to use GitHub Copilot Chat here is to provide only schema- level information such as table names, column names, relationships, and the required procedure behavior, without sharing actual table contents or result sets. That lets Copilot help generate and review the Transact- SQL while avoiding disclosure of customer data. This is consistent with Microsoft and GitHub guidance that content provided in prompts is what the AI can use, so avoiding real data in the prompt is the appropriate control.
The other options violate the requirement:
* A pastes real rows containing email addresses, which is direct PII disclosure.
* B shares actual query result sets, which the policy forbids.
* C provides the connection string so Copilot can validate against the database, which is inappropriate because it exposes connection details and could enable access beyond schema-only assistance.
So the correct approach is to ask Copilot to generate the stored procedure using only the schema and requirements, not real customer data.


NEW QUESTION # 58
You have an Azure SQL database that contains a table named dbo.Orders.
You have an application that calls a stored procedure named dbo.usp_tresteOrder to insert rows into dbo.
Orders.
When an insert fails, the application receives inconsistent error details.
You need to implement error handling to ensure that any failures inside the procedure abort the transaction and return a consistent error to the caller.
How should you complete the stored procedure? To answer, drag the appropriate values to the correct targets, tach 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:

Explanation:

Explanation:
* After the INSERT # SET @OrderId = SCOPE_IDENTITY()
* Inside CATCH # IF @@TRANCOUNT > 0 ROLLBACK TRANSACTION
The correct drag-and-drop choices are:
* SET @OrderId = SCOPE_IDENTITY()
* IF @@TRANCOUNT > 0 ROLLBACK TRANSACTION
After the INSERT, the procedure should assign the newly generated identity value to the output parameter by using SCOPE_IDENTITY() . Microsoft documents that SCOPE_IDENTITY() returns the last identity value inserted in the same scope, which makes it the correct choice for returning the new OrderId from the procedure.
Inside the CATCH block, the procedure should use IF @@TRANCOUNT > 0 ROLLBACK TRANSACTION before THROW. This ensures any open transaction is rolled back only when one actually exists, which prevents transaction-state issues and guarantees the failure aborts the transaction cleanly.
Keeping THROW after the rollback is also the correct modern pattern because THROW re-raises the error to the caller with the original error information intact, giving consistent error behavior. This matches SQL Server best practice for TRY...CATCH transaction handling.


NEW QUESTION # 59
You need to generate embeddings to resolve the issues identified by the analysts. Which column should you use?

Answer: C

Explanation:
The correct column to use for generating embeddings is incidentDescrlption because embeddings are intended to represent the semantic meaning of rich textual content , not simple categorical, numeric, or location-only values. Microsoft's DP-800 study guide explicitly includes skills such as identifying which columns to include in embeddings , generating embeddings , and implementing semantic vector search for scenarios where users need to find similar records based on meaning rather than exact matches.
In this scenario, analysts report that it is difficult to find similar incidents based on details such as weather, traffic conditions, and location . Those are descriptive context elements that are typically captured in a free- text incident description field. An embedding generated from incidentDescrlption can encode the semantic relationships among these narrative details, making it suitable for similarity search , semantic search , and RAG retrieval . Microsoft documentation on vectors and embeddings explains that embeddings are generated from text data and then stored for vector search to find semantically related items.
The other options are weaker choices:
* vehicleLocation is too narrow and usually better handled with geospatial filtering , not embeddings.
* incidentType is likely categorical and too low in semantic richness.
* SeverityScore is numeric and not appropriate as the primary source for semantic embeddings.
Microsoft also notes that when multiple useful attributes exist, you can either embed each text column separately or concatenate relevant text fields into one textual representation before generating the embedding.
But among the options given, the best and most exam-aligned answer is the textual narrative column :
incidentDescrlption .


NEW QUESTION # 60
......

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