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Microsoft DP-800 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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.
Topic 2
  • 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 3
  • 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.

Microsoft Developing AI-Enabled Database Solutions Sample Questions (Q94-Q99):

NEW QUESTION # 94
Case Study 1 - Contoso
Existing Environment
Azure Environment
Contoso has an Azure subscription in North Europe that contains the corporate infrastructure.
The current infrastructure contains a Microsoft SQL Server 2017 database. The database contains the following tables.

The FeedbackJsoncolumn has a full-text index and stores JSON documents in the following format.

The support staff at Contoso never has the UNMASKpermission.
Problem Statements
Contoso is deploying a new Azure SQL database that will become the authoritative data store for the following:
* AI workloads
* Vector search
* Modernized API access
* Retrieval Augmented Generation (RAG) pipelines
Sometimes the ingestion pipeline fails due to malformed JSON and duplicate payloads.
The engineers at Contoso report that the following dashboard query runs slowly.

You review the execution plan and discover that the plan shows a clustered index scan.
VehicleIncidentReportsoften contains details about the weather, traffic conditions, and location. Analysts report that it is difficult to find similar incidents based on these details.
Requirements
Planned Changes
Contoso wants to modernize Fleet Intelligence Platform to support AI-powered semantic search over incident reports.
Security Requirements
Contoso identifies the following security requirements:
* Restrict the support staff from viewing Personally Identifiable Information (PII) data, which is full email addresses and phone numbers.
* Enforce row-level filtering so that analysts see only incidents for the fleets to which they are assigned. The analysts can be assigned to multiple fleets.
Database Performance and Requirements
Contoso identifies the following telemetry requirements:
* Telemetry data must be stored in a partitioned table.
* Telemetry data must provide predictable performance for ingestion and retention operations.
* latitude, longitude, and accuracyJSON properties must be filtered by using an index seek.
Contoso identifies the following maintenance data requirements:
* Ensure that any changes to a row in the MaintenanceEventstable updates the corresponding value in the LastModifiedUtccolumn to the time of the change.
* Avoid recursive updates.
AI Search, Embeddings, and Vector Indexing
Contoso plans to implement semantic search over incident data to meet the following requirements:
* Embeddings must be stored in dedicated Azure SQL Database tables.
* Embeddings must be generated from rich natural language fields.
* Chunking must preserve semantic coherence.
* Hybrid search must combine the following:
- Vector similarity
- Keyword filtering or boosting
Development Requirements
The development team at Contoso will use Microsoft Visual Studio Code and GitHub Copilot and will retrieve live metadata from the databases.
Contoso identifies the following requirements for querying data in the FeedbackJsoncolumn of the CustomerFeedbacktable:
* Extract the customer feedback text from the JSON document.
* Filter rows where the JSON text contains a keyword.
* Calculate a fuzzy similarity score between the feedback text and a known issue description.
* Order the results by similarity score, with the highest score first.
Drag and Drop Question
You need to meet the database performance requirements for maintenance data.
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. 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 # 95
Your development team uses Microsoft Visual Studio Code with the MSSQL extension and the GitHub Copilot Chat extension.
The team connects to an Azure SQL database by using individual database logins and uses the
@mssql chat participant to generate and run Transact-SQL queries from prompts.
What is used to ensure that GitHub Copilot Chat-generated queries run in the context of the developer?

Answer: D

Explanation:
To ensure that GitHub Copilot Chat-generated queries run in the context of a specific developer when using the @mssql chat participant, SQL Permissions must be used.
Why SQL Permissions are the Key
When the @mssql extension executes a query generated by Copilot, it uses the active connection currently established in VS Code. Because your team uses individual database logins, the execution context is governed by the following:
Authentication: The developer logs in with their specific credentials.
Authorization: The SQL Server engine checks the SQL Permissions (GRANT/DENY/REVOKE) assigned to that specific database user.
Execution: Any T-SQL command sent by the Copilot chat participant is limited by what that specific login is allowed to do (e.g., SELECT, UPDATE, or DROP).
Reference:
https://learn.microsoft.com/en-us/sql/tools/visual-studio-code-extensions/github-copilot/limitations- and-known-issues


NEW QUESTION # 96
You have a GitHub Actions workflow that builds and deploys an Azure SQL database. The schema is stored in a GitHub repository as an SDK-style SQL database project.
Following a code review, you discover that you need to generate a report that shows whether the production schema has diverged from the model in source control.
Which action should you add to the pipeline?

Answer: B

Explanation:
To generate a report showing the differences between your source control and the production database, you should use the sql-action (officially Azure/sql-action@v2).
Specifically, you need to set the action parameter to DriftReport.
When configured this way, the action compares your .dacpac file (built from your SQL project) against the target production database. It generates an HTML or XML report detailing any "drift"-- changes made directly to production that aren't in your code.
Reference:
https://learn.microsoft.com/en-us/sql/tools/sqlpackage/sqlpackage-deploy-drift-report


NEW QUESTION # 97
Hotspot Question
You have a SQL database in Microsoft Fabric named SalesDB that contains a table named dbo.Products.
You need to modify SalesDB to meet the following requirements:
- Create a vector index on the appropriate column.
- Use a supplied natural language query vector.
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 # 98
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 # 99
......

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