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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.

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Microsoft Developing AI-Enabled Database Solutions Sample Questions (Q97-Q102):

NEW QUESTION # 97
Which scenario best fits AI-enhanced SQL querying?

Answer: A

Explanation:
AI allows conversational querying of SQL databases.


NEW QUESTION # 98
You have an SDK-style SQL database project named MyDatabaseProject.sqlproj stored in a private GitHub repository. The repository contains the following GitHub Actions workflow:
YAML
name: Build and Deploy SQL Project
on:
push:
branches:
- main
workflow_dispatch:
jobs:
build-and-deploy:
runs-on: ubuntu-latest
permissions:
id-token: write
contents: read
steps:
- uses: actions/checkout@v4
- name: Build SQL project
run: dotnet build MyDatabaseProject.sqlproj
- name: Publish DACPAC
uses: azure/sql-action@v2

Answer:

Explanation:

Explanation:


NEW QUESTION # 99
Drag and Drop Question
You have an Azure SQL database that contains a table named dbo.Orders. dbo.Orders contains a column named CreateDate that stores order creation dates.
You need to create a stored procedure that filters Orders by CreateDate for a single calendar day. The solution must be SARGable.
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 # 100
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_GENERME_EMBEDOINGS 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: A

Explanation:
When you change embedding models, the stored vectors should be treated as belonging to a different embedding space unless you intentionally keep the entire corpus consistent. Microsoft's vector guidance notes that when most or all embeddings are replaced with fresh embeddings from a new model, the recommended practice is to reload the new embeddings and, for large-scale replacement scenarios, consider dropping and recreating the vector index afterward so search quality remains predictable.
This question also says applications must continue to use VECTOR_SEARCH without runtime errors.
VECTOR_SEARCH requires compatible vector dimensions, and the vector column already exists. Azure OpenAI documentation shows that text-embedding-ada-002 is fixed at 1536 dimensions and text-embedding-
3-small supports up to 1536 dimensions. That means the migration can remain compatible with a VECTOR (1536) column, but the right implementation step is still to re-embed the existing rows so the table does not contain a mixed corpus produced by different models.
The other options are not appropriate:
B normalization does not solve a model migration problem.
C converting the vector column to nvarchar(max) would break vector-native search design.
D a vector index improves performance, but it does not migrate old embeddings to the new model.


NEW QUESTION # 101
You have an SDK-style SOL database project stored in a Git repository. The project targets an Azure SQL database.
The Cl build fails with unresolved reference errors when the project references system objects.
You need to update the SQL database project to ensure thai dotnet build validates successfully by including the correct system objects in the database model for Azure SQL Database.
Solution: Build the project by running dotnet build -bl -flp:v-diag.
Does this meet the goal?

Answer: B


NEW QUESTION # 102
......

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