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Microsoft DP-800 Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 3	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.

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Authentic DP-800 Exam Questions - DP-800 Latest Test Dumps

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Microsoft Developing AI-Enabled Database Solutions Sample Questions (Q46-Q51):

NEW QUESTION # 46
You have an Azure SQL database that supports an AI-enabled product catalog API. The database contains a table named Sales.Orders. Sales.Orders contains 20 million rows and has a nonclustered index on a column named CreateDate.
The API passes a date parameter named @OrderDate.
You have a stored procedure named get_latest_day_orders that filters by running the following query.
WHERE CAST(CreateDate AS date) = @0rderDate.
A review of the query execution reveals that the query causes an index scan.
You need to modify the predicate to ensure that Microsoft SQL Server can use an index seek on CreateDate for a single day.
Which WHERE clause should you use?

A. WHERE CONVERT(char(10), CreateDate, 121) = CONVERT(char(10), @OrderDate, 121)
B. WHERE LEFT(CONVERT(varchar(30), CreateDate, 121), 10) = CONVERT(char(10),
@0rderDate, 121)
C. WHERE CreateDate LIKE CONVERT(char(10), @0rderDate, 121) + '%'
D. WHERE CreateDate >= @OrderDate AND CreateDate < DATEADD(day, 1, @0rderDate)

Answer: D

Explanation:
To achieve an Index Seek on your CreateDate column, you must remove the CAST function from the column side of the equation. Wrapping a column in a function makes the expression non- SARGable (Searchable Argument), forcing SQL Server to scan the entire index rather than jumping to a specific range.
The Recommended WHERE Clause
To capture all rows for a single day while remaining SARGable, use a range comparison:
WHERE CreateDate >= @OrderDate
AND CreateDate < DATEADD(day, 1, @OrderDate)
Why This Works
Eliminates Functions on Columns: By keeping CreateDate "naked," the query optimizer can use the nonclustered index to find the exact starting point.
Handles Time Components: Since CreatedDate likely contains time data (DATETIME or DATETIME2), this logic captures everything from 00:00:00.000 up to (but not including) midnight of the following day.
Single Day Precision: It creates a "half-open" interval that is the standard best practice for date filtering in SQL Server.
Reference:
https://medium.com/@c.charalambos1998/how-to-design-indexes-in-sql-server-for-faster-query- performance-4a2279a9b512

NEW QUESTION # 47
What is the role of Azure Synapse in AI + SQL solutions?

A. DNS management
B. Email services
C. Data warehousing and analytics
D. Authentication

Answer: C

Explanation:
Azure Synapse Analytics combines big data and data warehousing for AI-driven insights.

NEW QUESTION # 48
You have a SQL database in Microsoft Fabric that contains a column named Payload. pay load stores customer data in JSON documents that have the following format.

Data analysis shows that some customers have subaddressing in their email address, for example, user1+promo@contoso.com.
You need to return a normalized email value that removes the subaddressing, for example, user! + promo@contoso.com must be normalized to userl@contoso.com.
Which Transact SQL expression should you use?

A. REGEXP_REPLACE(JSON_VALUE(Payload, ' $.customer_email ' ), ' \+.*@ ' , ' @ ' )
B. REGEXP_REPLACE(JSON_VALUE(Payload, ' $.customer_email ' ), ' \+.*$ ' , ' ' )
C. REGEXP_SUBSTR(JSON_VALUE(Payload, ' $.customer_email ' ), '
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