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Microsoft DP-800 시험요강:

주제소개
주제 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.
주제 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.
주제 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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최신 Microsoft Certified: SQL AI Developer DP-800 무료샘플문제 (Q27-Q32):

질문 # 27
You have a SQL database in Microsoft Fabric that contains a column named Payload. Payload stores customer data in JSON documents that have the following format:
JSON
{
" date " : " 2026-01-25 " ,
" customer_email " : " user@contoso.com " ,
...
}
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, user1+promo@contoso.com must be normalized to user1@contoso.com.
Which Transact-SQL expression should you use?

정답：B


질문 # 28
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.

정답：

설명：

Explanation:
* ON # m.maintenanceId = i.maintenanceId
* WHERE # m.LastModifiedUtc < > i.LastModifiedUtc
The correct drag-and-drop completion is:
* ON m.maintenanceId = i.maintenanceId
* WHERE m.LastModifiedUtc < > i.LastModifiedUtc
This satisfies the requirement to ensure that when a row in MaintenanceEvents changes, the corresponding LastModifiedUtc value is updated to the current system time, while also helping avoid unnecessary repeat updates.
The inserted pseudo-table in a SQL Server AFTER UPDATE trigger contains the rows that were just updated.
To update the matching row in the base table correctly, the trigger must join the target table row to the corresponding row in inserted by the table's primary key. In this schema, MaintenanceId is the primary key for MaintenanceEvents, so the correct join is m.maintenanceId = i.maintenanceId . Joining on VehicleId would be incorrect because multiple maintenance rows could exist for the same vehicle, which could update unintended rows. Microsoft's trigger documentation explains that inserted and deleted are used to work with the affected rows and that multi-row logic should be based on proper key matching.
The WHERE m.LastModifiedUtc < > i.LastModifiedUtc predicate is used to prevent the trigger from re- updating rows where the timestamp already matches the value in inserted. That reduces redundant writes and supports the requirement to avoid recursive or repeated update behavior. In practice, this means the trigger updates only rows whose current stored timestamp differs from the just-updated version. This is the exam- appropriate pattern for a self-updating timestamp column in an AFTER UPDATE trigger.


질문 # 29
You have an Azure SQL database named SalesDB that contains a table named dbo. Articles, dbo.Articles contains two million articles with embeddmgs. The articles are updated frequently throughout the day.
You query the embeddings by using VECTOR_SEARQi
Users report that semantic search results do NOT reflect the updates until the following day.
Vou need to ensure that the embeddings are updated whenever the articles change. The solution must minimize CPU usage on SalesDB Which embedding maintenance method should you implement?

정답：D

설명：
The correct answer is B because the problem is not the vector search operator itself. The problem is that embeddings are becoming stale when article content changes . Microsoft documents that change data capture (CDC) tracks insert, update, and delete operations on source tables, which makes it the right mechanism to identify only the rows that changed.
This also best satisfies the requirement to minimize CPU usage on SalesDB . With CDC, the database only records the row changes, and the embedding regeneration work can be moved to an external process such as an Azure Functions app. That avoids running embedding generation inline inside the database for every update and avoids repeatedly recalculating embeddings for unchanged rows. In contrast, an hourly full-table regeneration would be extremely wasteful on a table with two million frequently updated articles, and a trigger that calls embedding generation per row would push expensive AI work into the transactional path of the database.
Option A is incorrect because changing from VECTOR_SEARCH to VECTOR_DISTANCE does not regenerate embeddings; it only changes the retrieval method. Microsoft states that VECTOR_SEARCH is the ANN search function, while VECTOR_DISTANCE performs exact distance calculation, so neither option addresses stale embedding data.
So the right design is:
* use CDC to detect only changed articles,
* process those changes outside the database,
* regenerate embeddings only for changed rows,
* write back the refreshed embeddings for current semantic search results.


질문 # 30
You have an Azure container app named app1-contoso-001 that hosts a Data API builder (DAB) container in front of an Azure SQL database.
You add an entity named Todo that uses a source named dbo.todos.
Which URL pattern should clients use to access the Todo REST endpoint?

정답：A

설명：
The correct answer is B because Data API builder exposes REST entities by default using the URL pattern:
https://{base_url}/api/{entity}
Microsoft's current DAB documentation states exactly that the default REST endpoint pattern is /api/{entity}
. For an entity named Todo , the REST route is therefore /api/Todo .
The other options are incorrect:
* A uses a GraphQL-style route and is not the REST endpoint.
* C uses /data-api/Todo , which is not the default REST path for a directly hosted DAB runtime.
* D adds an unnecessary /data-api prefix before /api/Todo .
Microsoft also shows examples such as:
GET https://localhost:5001/api/book
and documents collection reads as:
GET /api/{entity} .
So for the entity configuration:
* Entity name: Todo
* Source: dbo.todos
the REST endpoint path is:
/api/Todo
Therefore, among the supplied choices, Option B is the correct answer.


질문 # 31
You need to recommend a solution for the development team to retrieve the live metadata. The solution must meet the development requirements.
What should you include in the recommendation?

정답：B


질문 # 32
......

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