DP-800熱門考古題將是您最好的助力Developing AI-Enabled Database Solutions

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>> DP-800熱門考古題 <<

最新的DP-800認證考試考古題

KaoGuTi的DP-800考古題有著讓你難以置信的命中率。這個考古題包含實際考試中可能出現的一切問題。因此，只要你好好學習這個考古題，通過DP-800考試就會非常容易。作為Microsoft的一項重要的考試，DP-800考試的認證資格可以給你帶來很大的好處。所以你絕對不能因為失去這次可以成功通過考試的機會。KaoGuTi承諾如果考試失敗就全額退款。為了你能順利通過DP-800考試，趕緊去KaoGuTi的網站瞭解更多的資訊吧。

Microsoft DP-800 考試大綱：

主題簡介
主題 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.
主題 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.
主題 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.

最新的 Microsoft Certified: SQL AI Developer DP-800 免費考試真題 (Q26-Q31):

問題 #26
You have a SQL database in Microsoft Fabric that contains a table named dbo.Orders, dbo.Orders has a clustered index, contains three years of data, and is partitioned by a column named OrderDate by month.
You need to remove all the rows for the oldest month. The solution must minimize the impact on other queries that access the data in dbo.orders.
Solution; Identify the partition scheme (or the oldest month, and then run the following Transact-SQL statement.
ALTER TABLE dbo.Orders
DROP PARTITION SCHEME (partition_scheme_name);
Does this meet the goal?

答案：B

解題說明：
This also does not meet the goal. DROP PARTITION SCHEME removes the partition scheme object from the database; it is not the command used to remove just the rows for the oldest month from a partitioned table.
Microsoft's DROP PARTITION SCHEME documentation is explicit that the statement removes the partition scheme itself.
For removing only the oldest month's rows with minimal impact, Microsoft points to partition-level maintenance operations such as truncating a single partition on a partitioned table. That targets only the needed data subset and is more efficient for retention workloads.


問題 #27
You need to recommend a solution to lesolve the slow dashboard query issue. What should you recommend?

答案：B

解題說明：
The best recommendation is B because the slow query filters on FleetId and returns LastUpdatedUtc , EngineStatus , and BatteryHealth . A nonclustered index with FleetId as the key column allows the optimizer to perform an index seek instead of a clustered index scan, and including the other selected columns makes the index covering , which reduces extra lookups and I/O. Microsoft's SQL Server indexing guidance states that a nonclustered index with included columns can significantly improve performance when all query columns are available in the index, because the optimizer can satisfy the query directly from the index.
The query is:
SELECT VehicleId, LastUpdatedUtc, EngineStatus, BatteryHealth
FROM dbo.VehicleHealthSummary
WHERE FleetId = @FleetId
ORDER BY LastUpdatedUtc DESC;
Among the given choices, FleetId is the most important search argument because it appears in the WHERE predicate. Microsoft's index design guidance recommends putting columns used for searching in the key and using nonkey included columns to cover the rest of the query efficiently.
Why the other options are weaker:
* A is not appropriate because changing the clustered index to LastUpdatedUtc would not target the main filter predicate on FleetId, and a table can have only one clustered index.
* C makes LastUpdatedUtc the key, which is poor for a query whose primary filter is FleetId.
* D is not the right answer here because the query requirement does not specify only recent rows, and filtered indexes are meant for a well-defined subset; this option also uses a time-based expression that is not aligned to the stated query pattern.
Strictly speaking, the most optimal design for both filtering and ordering would usually be a composite key like (FleetId, LastUpdatedUtc), but since that is not one of the available options, B is the correct exam answer.


問題 #28
You have an Azure SQL database that contains a table named Rooms. Rooms was created by using the following transact-SQL statement.

You discover that some records in the Rooms table contain NULL values for the Owner field. You need to ensure that all future records have a value for the Owner field. What should you add?

答案：C

解題說明：
The table definition allows Owner to be nullable because it was created as Owner nvarchar(100) without NOT NULL. Since the question asks what to add so that future rows must have a value, a check constraint such as CHECK (Owner IS NOT NULL) is the appropriate choice. Microsoft documents that check constraints validate future INSERT and UPDATE operations against the constraint condition.
The other options do not solve the requirement:
* A foreign key enforces referential integrity, not non-null entry by itself.
* A nonclustered index does not require values to be present.
* A unique constraint prevents duplicate values but still does not serve as the right mechanism here for enforcing presence across future writes. Microsoft's constraint documentation also notes that primary- key columns are implicitly NOT NULL, which helps distinguish nullability enforcement from other constraint types.


問題 #29
You have an SDK-style SQL database project stored in a Git repository. The project targets an Azure SQL database.
The CI build fails with unresolved reference errors when the project leferences system objects.
You need to update the SQL database project to ensure that dotnet build validates successfully by including the correct system objects in the database model for Azure SQL Database.
Solution: Add an artifact reference to the Azure SQL Database master.dacpac file.
Does this meet the goal?

答案：B

解題說明：
For an SDK-style SQL database project targeting Azure SQL Database , Microsoft recommends using the Azure SQL system DACPAC as a NuGet package reference rather than adding a direct artifact reference to master.dacpac for new SDK-style development. Microsoft's SQL Database Projects documentation says direct .dacpac artifact references are not recommended for new development in SDK-style projects ; instead, use NuGet package references .
Because the goal is specifically to make dotnet build validate successfully with the correct Azure SQL system objects , adding an artifact reference to master.dacpac is not the recommended SDK-style solution. It can work in some project styles, but it does not meet the stated goal as the proper approach for SDK-style Azure SQL projects.


問題 #30
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?

答案：B

解題說明：
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.


問題 #31
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