DP-800 무료 덤프문제 온라인 액세스

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?

• A.Convert the Embedding column to nvacchar(mex).
• B.Normalize the vector lengths before storing new embeddings.
• C.Regenerate embeddings for the existing rows.
• D.Create a vector index on dbo.Products.Embedding.

You have an Azure SQL database that contains the following tables and columns.

Embeddings in the NotesEnbeddings and DescriptionEabeddings tables have been generated from values in the Description and notes columns of the Articles table by using different chunk sizes.
You need to perform approximate nearest neighbor (ANN) queries across both embedding tables. The solution must minimize the impact of using different chunk sizes.
What should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

정답:

Explanation:

The correct function is VECTOR_SEARCH because the requirement is to perform approximate nearest neighbor (ANN) queries. Microsoft's SQL documentation states that VECTOR_SEARCH is the function used for vector similarity search, and that an ANN index is used only with VECTOR_SEARCH when a compatible vector index exists on the target column. By contrast, VECTOR_DISTANCE calculates an exact distance and does not use a vector index for ANN retrieval.
The correct distance metric is cosine distance. Microsoft documents that VECTOR_SEARCH supports cosine, dot, and euclidean metrics, and Microsoft guidance specifically notes that cosine similarity is commonly used for text embeddings. It also states that retrieval of the most similar texts to a given text typically functions better with cosine similarity, and that Azure OpenAI embeddings rely on cosine similarity to compute similarity between a query and documents. Since both NotesEmbeddings and DescriptionEmbeddings are text-derived embeddings and the goal is to minimize the impact of different chunk sizes, cosine is the best choice because it compares direction/angle rather than being as sensitive to vector magnitude as Euclidean distance.

You have a database named db1. The schema is stored in a Git repository as an SDK-style SQL database project The repository Contains the following GitHub Action workflow.

For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.

정답:

Explanation:
* Unit tests run automatically whenever changes are pushed to main. # Yes
* Schema validation occurs during the Build step. # Yes
* Schema validation occurs during the Deploy step. # No
The first statement is Yes . The workflow is configured to trigger on both push to main and pull_request targeting main. The unit-tests job has this condition:
if: github.ref == ' refs/heads/main '
On a push to main , GitHub sets github.ref to refs/heads/main, so the condition is true and the unit-tests job runs. GitHub's workflow syntax documentation confirms that push.branches: [main] triggers on pushes to main, and the github.ref value for branch pushes is the fully qualified ref such as refs/heads/main.
The second statement is Yes . The Build step runs:
dotnet build db1.sqlproj --configuration Release
For an SDK-style SQL database project, the build process produces a .dacpac and validates the database project model as part of compilation/build. Microsoft's SQL database project documentation describes SDK- style SQL projects as the project format used for SQL Database Projects, and Microsoft's command-line build documentation is specifically about building a .dacpac from that SQL project. That means schema-level project validation happens during build.
The third statement is No . The Deploy step uses:
SqlPackage /Action:Publish ...
Microsoft documents that SqlPackage Publish incrementally updates the target database schema to match the source .dacpac. That is a deployment operation, not the primary schema-validation stage of the SQL project source itself. In this workflow, the schema is validated when the SQL project is built into the .dacpac; the deploy step applies that built artifact to the target database.

You have an Azure SQL database that contains tables named dbo.ProduetDocs and dbo.
ProductuocsEnbeddings. dbo.ProductOocs contains product documentation and the following columns:
* Docld (int)
* Title (nvdrchdr(200))
* Body (nvarthar(max))
* LastHodified (datetime2)
The documentation is edited throughout the day. dbo.ProductDocsEabeddings contains the following columns:
* Dotid (int)
* ChunkOrder (int)
* ChunkText (nvarchar(aax))
* Embedding (vector(1536))
The current embedding pipeline runs once per night
Vou need to ensure that embeddings are updated every time the underlying documentation content changes The solution must NOT ' equire a nightly batch process.
What should you include in the solution?

• A.a smaller embedding model
• B.fixed-size chunking
• C.table triggers
• D.change tracking on dbo.ProductDocs

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:

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.