DP-800 Exam Dumps - Developing AI-Enabled Database Solutions

The correct answer is D because the requirement is to enable similarity search over embedding vectors and to minimize latency . Microsoft documents that CREATE VECTOR INDEX is specifically used to create an index on vector data for approximate nearest neighbor (ANN) search , which is designed to accelerate vector similarity queries compared to exact k-nearest-neighbor scans.

This matches the scenario exactly. The VehicleHealthSummary table already includes an Embeddings (vector(1536)) column. In Microsoft SQL platforms, embeddings are stored in vector columns and queried for semantic similarity. To improve performance and reduce response time, Microsoft recommends a vector index , not a regular B-tree nonclustered index and not a full-text index. A vector index is purpose-built for finding the most similar vectors efficiently.

The other options are not appropriate:

A would require manual comparison logic and would increase latency rather than minimize it.

B is incorrect because a standard nonclustered index is not the index type used for vector similarity operations.

C is incorrect because full-text indexes are for textual token-based search, not numeric vector embeddings.

Microsoft’s current documentation is explicit that vector indexes support approximate nearest neighbor search , and that the optimizer can use the ANN index automatically for vector queries. That is the exam-aligned design choice when the goal is fast retrieval of the most relevant health summary reports from an embeddings column.

The package named Microsoft.SqlServer.Dacpacs.Master is the generic master system DACPAC package, but the question requires the correct system objects for Azure SQL Database . Microsoft’s system-objects documentation distinguishes platform-specific system references, and for Azure SQL Database the correct package is the Azure-specific master DACPAC , not the generic master package.

So adding Microsoft.SqlServer.Dacpacs.Master does not meet the goal for an Azure SQL Database-targeted SDK-style project. The expected package is the Azure-specific one.

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.

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.

The correct answer is C because the email must be normalized by removing only the subaddressing portion between the plus sign and the @, while preserving the domain. JSON_VALUE is the correct function to extract the scalar email value from the JSON document. Microsoft states that JSON_VALUE is used to extract a scalar value from JSON text.

Then REGEXP_REPLACE should remove the pattern \+.*@ and replace it with a single @. For example:

user1+promo@contoso.com → user1@contoso.com

Microsoft documents that REGEXP_REPLACE returns the source string with text matching the regular expression replaced by the replacement string, and that an empty or custom replacement string can be used to reshape the result.

Why the other options are wrong:

A removes everything from + to the end of the string, which would leave user1 and lose @contoso.com.

B tries to extract a string that already excludes +..., but it does not reliably reconstruct the normalized address in this pattern.

D also removes everything after +, including the domain, which is incorrect.

So the normalized-email expression is:

REGEXP_REPLACE(JSON_VALUE(Payload, ' $.customer_email ' ), ' \+.*@ ' , ' @ ' )

Serialize the order-level JSON : FOR JSON PATH

Generate a nested lines array : JSON_QUERY

Extract a single scalar value from the JSON text : JSON_VALUE

The correct mapping is based on how SQL Server and Azure SQL JSON functions are designed to shape relational data into JSON for AI and RAG scenarios.

To serialize the order-level JSON , use FOR JSON PATH . Microsoft documents that FOR JSON PATH gives you full control over the JSON output shape and formats the result as an array of JSON objects . It is the standard way to turn relational query results into the JSON structure needed by downstream consumers such as APIs and LLM-based RAG services. It also supports nested output through subqueries and aliases.

To generate a nested lines array , use JSON_QUERY . Microsoft explains that JSON_QUERY returns a JSON object or array from JSON text, and it is used when you want to preserve a JSON fragment instead of treating it as plain text. In this scenario, the nested lines property must be emitted as a proper JSON array inside each order document, so JSON_QUERY is the correct command to embed that array in the final JSON shape.

To extract a single scalar value from the JSON text , use JSON_VALUE . Microsoft explicitly states that JSON_VALUE extracts a scalar value from a JSON string, while JSON_QUERY is for objects or arrays. So whenever the requirement is to pull out one property such as an order number, currency code, or customer ID from JSON text, JSON_VALUE is the correct function.

The unused commands are not the best fit here:

OPENJSON is primarily for parsing JSON into rows and columns, not for shaping relational tables into nested output.

JSON_MODIFY is for updating JSON text, not generating the required output structure.

So the drag-and-drop answers are:

Serialize the order-level JSON → FOR JSON PATH

Generate a nested lines array → JSON_QUERY

Extract a single scalar value from the JSON text → JSON_VALUE

The requirement is very specific: prevent ddl_Audit from firing during the next deployment , but leave the trigger object in place . Microsoft documents DISABLE TRIGGER as the statement used to disable a trigger without dropping it. That is exactly the right operation for a temporary suspension of a DDL trigger. For a database-scoped DDL trigger, the syntax is on the database scope, for example DISABLE TRIGGER ddl_Audit ON DATABASE;.

The other options do not meet the requirement as directly:

ALTER TRIGGER changes the trigger definition, not simply disables execution.

ALTER DATABASE is not the direct statement for disabling a specific DDL trigger.

ALTER SERVER AUDIT SPECIFICATION and ALTER DATABASE AUDIT SPECIFICATION are audit-feature statements, not trigger-control statements.

So the correct Transact-SQL statement is DISABLE TRIGGER .

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.