Microsoft DP-800 Developing AI-Enabled Database Solutions Exam Practice Test

where exists 2. where p.PatientID = pr.PatientID Otherwise we filter PatientID twice. Having in the subquery does not work without group by and the date filter does not work without where.

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

CTE inner source → FROM sys.dm_exec_requests

Join after sys.dm_exec_sessions AS s → LEFT OUTER JOIN sys.dm_exec_requests

Text retrieval → OUTER APPLY sys.dm_exec_sql_text(r.sql_handle)

Input buffer retrieval → OUTER APPLY sys.dm_exec_input_buffer(r.session_id, r.request_id)

The correct drag-and-drop choices are based on how blocking-chain details are normally assembled in Azure SQL Database.

The CTE must read from sys.dm_exec_requests because the alias er is used with er.session_id and er.blocking_session_id, and those columns come from sys.dm_exec_requests. Microsoft documents that sys.dm_exec_requests returns information about executing requests and includes the blocking_session_id column used to identify blockers.

After FROM sys.dm_exec_sessions AS s, the correct join is LEFT OUTER JOIN sys.dm_exec_requests so the query can still return sessions from sys.dm_exec_sessions even when a current request row is missing. This is useful when showing sessions that are blocked or blocking, while still attempting to attach current request details when available.

For batch text, use OUTER APPLY sys.dm_exec_sql_text(r.sql_handle) because Microsoft documents sys.dm_exec_sql_text(sql_handle) as the function that returns the SQL batch text for the specified sql_handle.

For the input buffer, use OUTER APPLY sys.dm_exec_input_buffer(r.session_id, r.request_id) because Microsoft documents that sys.dm_exec_input_buffer takes session_id and request_id and returns event_info, which is commonly used when sys.dm_exec_sql_text is null or when you want the last command text.

So the completed code uses:

FROM sys.dm_exec_requests

LEFT OUTER JOIN sys.dm_exec_requests

OUTER APPLY sys.dm_exec_sql_text(r.sql_handle)

OUTER APPLY sys.dm_exec_input_buffer(r.session_id, r.request_id)

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.

Yes, this meets the goal. Microsoft documents that on a partitioned table , you can use TRUNCATE TABLE ... WITH (PARTITIONS (...)) to remove data from a specific partition, and that this is an efficient maintenance operation that targets only that data subset rather than the whole table. Microsoft’s partitioning guidance explicitly lists truncating a single partition as an example of a fast partition-level maintenance or retention operation.

That matches the requirement to remove the oldest month while minimizing impact on other queries. Because the table is already partitioned by month on OrderDate , identifying the partition number for that oldest month and truncating only that partition is the correct low-impact approach, assuming the table and indexes are aligned as required for partition truncation.

The correct recommendation is to retrieve grounding data from knowledge_base and, at inference time, generate query embeddings and run a vector similarity search .

The chatbot currently answers some general HR questions but fails when policies change and when users ask about internal policy documents by title or category . That is exactly the kind of problem RAG is meant to solve: ground the LLM in the organization’s proprietary content instead of relying on the model’s training data or unrelated transactional tables. Microsoft’s RAG guidance states that RAG extends LLMs by grounding responses in your own content and that, for agentic retrieval, knowledge bases unify knowledge sources for retrieval.

So the grounding data should come from knowledge_base , because that table stores the HR policy documents and already includes fields like title, content, category, and embedding. Those are the fields directly tied to the missing and outdated policy answers. By contrast:

employee_profiles and benefits_enrollment are operational HR tables, not the authoritative store for policy-document grounding.

PDF exports of the policies would be inferior to querying the indexed/structured knowledge base already prepared for retrieval.

The LLM training data is specifically the wrong source when the issue is outdated internal content.

For the retrieval step, Microsoft’s guidance says to use embeddings for vector queries and notes that vector similarity search matches concepts, not exact terms . This is especially important because users ask about policy documents by title or category and also phrase questions in ways that might not exactly match document wording. Generating a query embedding and then running a vector similarity search is the appropriate retrieval step in a RAG pipeline.

The correct answer is B . Microsoft’s SSMS Copilot documentation states that queries from Copilot in SSMS are executed under the context of the user’s login and permissions , and that there are no separate permissions for Copilot in SSMS . That means Copilot-run Transact-SQL cannot access more data than the developer’s own database principal is already allowed to access.

That is why the other options are incorrect:

A is wrong because Copilot does not use a separate read-only sandbox in place of database permissions.

C is wrong because enforcement is not a client-side filtering trick; it is enforced by the database security context of the current login.

D is wrong because Copilot does not apply a different RLS model from the developer; it simply runs under the same login context.

So the security boundary is the developer’s existing database identity and permissions .

The correct SARGable pattern for filtering a single calendar day is to use a half-open date range :

o.CreateDate > = @StartDate

AND o.CreateDate < @EndDate

with:

SET @EndDate = DATEADD(day, 1, @StartDate)

This is the correct design because it keeps the function off the column and applies it only to the parameter. That allows SQL Server and Azure SQL to use an index on CreateDate efficiently, which is the key requirement for a SARGable predicate. Microsoft documents DATEADD as the standard function for adding one day to a date value, which makes it the right way to derive the exclusive upper boundary for the next day.

The incorrect choices are the ones that wrap CreateDate in CONVERT(...), because expressions like:

CONVERT(char(10), CreateDate, 121) = ...

make the predicate non-SARGable and typically prevent efficient seeks on an index over CreateDate.

So the completed procedure is:

CREATE PROCEDURE dbo.usp_SearchOrders

@StartDate date

AS

BEGIN

SET NOCOUNT ON;

DECLARE @EndDate date;

SET @EndDate = DATEADD(day, 1, @StartDate);

SELECT o.CreateDate,

o.OrderId,

o.ShipDate

FROM dbo.Orders AS o

WHERE o.CreateDate > = @StartDate

AND o.CreateDate < @EndDate;

END;

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 first statement is No . The requirement says telemetry data must be stored in a partitioned table to provide predictable performance for ingestion and retention operations. However, the shown CREATE TABLE statement does not define a partition function or partition scheme, and the table is created with a regular clustered primary key on TelemetryId. Microsoft’s partitioning guidance states that creating a partitioned table requires a partition function , a partition scheme , and creating the table or index on that partition scheme using a partitioning column. None of that appears in the code, so the table is not partitioned.

The second statement is Yes . The code creates a JSON index named JI_VehicleTelemetry_Location on LocationJson for these specific JSON paths: $.location.latitude, $.location.longitude, and $.location.accuracy. That matches the requirement that those JSON properties must be filterable by using an index seek . Microsoft documents that JSON indexing is used to optimize filtering and sorting on JSON properties, and the index only helps for the properties included in the index definition.

The third statement is No . The JSON index is defined only for latitude, longitude, and accuracy. A query filtering on $.location.heading references a different path that is not included in the index definition, so that query would not use JI_VehicleTelemetry_Location for that predicate. JSON indexes are path-specific; they do not automatically cover unrelated properties in the same JSON document.

JSON_VALUE(f.FeedbackJson, ' $.text ' ) AS FeedbackText

CONTAINS(FeedbackJson, @Keyword)

SimilarityScore

These three selections are the correct way to complete the query because they align exactly with the stated requirements for the FeedbackJson column.

First, to extract the customer feedback text from the JSON document , the correct expression is JSON_VALUE(f.FeedbackJson, ' $.text ' ) AS FeedbackText . Microsoft documents that JSON_VALUE is used to extract a scalar value from JSON, while JSON_QUERY is used for returning an object or array . Since $.text is the textual feedback string, JSON_VALUE is the correct function.

Second, to filter rows where the JSON text contains a keyword , the best choice is CONTAINS(FeedbackJson, @Keyword) . The scenario explicitly states that FeedbackJson already has a full-text index , and Microsoft documents that CONTAINS is the full-text predicate used in the WHERE clause to search full-text indexed character data. That makes it more appropriate than using EDIT_DISTANCE for keyword filtering.

Third, to order the results by similarity score, highest first , the correct item is SimilarityScore in the ORDER BY clause, which would be paired with DESC in the query. This matches the requirement to sort by the computed fuzzy similarity value. The DP-800 study guide specifically includes writing queries that use fuzzy string matching functions such as EDIT_DISTANCE, which supports the earlier computed SimilarityScore expression in the query.

The schema meets the security requirements for PII data. → Yes

Administrators of the Azure SQL server can see all the rows in dbo.CustomerProfiles when they use an application. → No

The masking rules will apply even when row-level security (RLS) filters out rows. → No

The first statement is Yes because the design combines two relevant SQL security controls for personally identifiable information: Dynamic Data Masking (DDM) on sensitive columns such as FullName, EmailAddress, and PhoneNumber, and Row-Level Security (RLS) to restrict which rows a user can access based on RegionCode. Microsoft documents that DDM limits sensitive data exposure for nonprivileged users , while RLS restricts row access according to the user executing the query. Together, these are valid and appropriate controls for protecting PII in Azure SQL Database.

The second statement is No . Administrative users can view unmasked data because administrative roles effectively have CONTROL, which includes UNMASK. However, that does not mean they automatically see all rows through the application query path defined by the RLS policy. The security policy filters rows based on SUSER_SNAME() and matching RegionCode, so row visibility is governed by the predicate unless the policy is altered or bypassed administratively. DDM and RLS solve different problems: DDM affects how returned values are shown, while RLS affects which rows are returned at all.

The third statement is No because masking only applies to data that is actually returned in the query result set. Microsoft describes DDM as hiding sensitive data in the result set of a query . If RLS filters a row out, that row is not returned, so there is nothing left for masking to act on. In other words, RLS eliminates inaccessible rows first from the user’s perspective, and DDM masks sensitive column values only on rows the user is allowed to see.

The best recommendation is to use an MCP server . In the official DP-800 study guide , Microsoft explicitly lists skills such as configuring Model Context Protocol (MCP) tool options in a GitHub Copilot session and connecting to MCP server endpoints, including Microsoft SQL Server and Fabric Lakehouse . That makes MCP the exam-aligned mechanism for enabling AI-assisted tools to work with live database context rather than static snapshots.

This also matches the stated development requirement: the team will use Visual Studio Code and GitHub Copilot and needs to retrieve live metadata from the databases . Microsoft’s documentation for GitHub Copilot with the MSSQL extension explains that Copilot works with an active database connection , provides schema-aware suggestions , supports chatting with a connected database, and adapts responses based on the current database context . Microsoft also documents MCP as the standard way for AI tools to connect to external systems and data sources through discoverable tools and endpoints.

The other options do not satisfy the “live metadata” requirement as well:

A .dacpac is a point-in-time schema artifact, not live metadata.

A Copilot instruction file provides guidance, not live database discovery.

Including the database project in the repository helps source control and deployment, but it still does not provide live database metadata by itself.

The correct column to use for generating embeddings is incidentDescrlption because embeddings are intended to represent the semantic meaning of rich textual content , not simple categorical, numeric, or location-only values. Microsoft’s DP-800 study guide explicitly includes skills such as identifying which columns to include in embeddings , generating embeddings , and implementing semantic vector search for scenarios where users need to find similar records based on meaning rather than exact matches.

In this scenario, analysts report that it is difficult to find similar incidents based on details such as weather, traffic conditions, and location . Those are descriptive context elements that are typically captured in a free-text incident description field. An embedding generated from incidentDescrlption can encode the semantic relationships among these narrative details, making it suitable for similarity search , semantic search , and RAG retrieval . Microsoft documentation on vectors and embeddings explains that embeddings are generated from text data and then stored for vector search to find semantically related items.

The other options are weaker choices:

vehicleLocation is too narrow and usually better handled with geospatial filtering , not embeddings.

incidentType is likely categorical and too low in semantic richness.

SeverityScore is numeric and not appropriate as the primary source for semantic embeddings.

Microsoft also notes that when multiple useful attributes exist, you can either embed each text column separately or concatenate relevant text fields into one textual representation before generating the embedding. But among the options given, the best and most exam-aligned answer is the textual narrative column : incidentDescrlption .

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.

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.

The two correct actions are D and E because the ingestion failures are caused by malformed JSON and duplicate payloads , and these two controls address those two problems directly. Microsoft’s JSON documentation states that SQL Server and Azure SQL support validating JSON with ISJSON , and Microsoft specifically recommends using a CHECK constraint to ensure JSON text stored in a column is properly formatted.

For the duplicate-payload issue, creating a unique index on a hash of the payload is the appropriate design. Microsoft documents using hashing functions such as HASHBYTES to hash column values, and SQL Server allows a deterministic computed column to be used as a key column in a UNIQUE constraint or unique index . That makes a persisted hash-based computed column plus a unique index a practical and exam-consistent way to reject duplicate payloads efficiently.

The other options do not solve the stated root causes:

Snapshot isolation addresses concurrency behavior, not malformed JSON or duplicate payload detection.

A trigger to rewrite malformed JSON is not the right integrity control and is brittle.

Foreign key constraints enforce referential integrity, not JSON validity or duplicate-payload prevention