Free Practice Questions for Microsoft DP-800 Exam

Microsoft documents REGEXP_SUBSTR for Transact-SQL with the string_expression parameter as supporting character string types char, nchar, varchar, and nvarchar. For the regex functions, support for LOB types such as varchar(max) and nvarchar(max) is specifically called out for REGEXP_LIKE , REGEXP_COUNT , and REGEXP_INSTR up to 2 MB, but that support note is not listed for REGEXP_SUBSTR in the surfaced documentation. In exam terms, the safe and expected approach is to cast the nvarchar(max) column to nvarchar(4000) before calling REGEXP_SUBSTR.

This also fits the scenario detail that the ID is always contained within the first paragraph of MessageText. Since the needed value is near the start of the text, narrowing the input to a non-LOB string type such as nvarchar(4000) is sufficient and avoids incompatibility concerns with nvarchar(max).

The other options are not appropriate:

A STRING_ESCAPE(..., ' json ' ) is for JSON escaping, not regex extraction.

C adding a case-sensitive collation changes comparison behavior, but it is not the required fix for REGEXP_SUBSTR on nvarchar(max).

D TRY_CONVERT(varchar(max), ...) still leaves a MAX type and also risks unnecessary Unicode loss.

The correct mapping is:

FOR JSON PATH

JSON_VALUE

JSON_QUERY

To serialize the retrieved rows from knowledge_base , the correct command is FOR JSON PATH . Microsoft documents that FOR JSON formats query results as JSON, and PATH mode is the standard way to shape relational rows into JSON for downstream application or AI use.

To extract the answer field from the response , the correct command is JSON_VALUE because answer is a single scalar field . Microsoft states that JSON_VALUE is used to extract a scalar value from JSON text.

To extract the embeddings to store in query_cache , the correct command is JSON_QUERY because embeddings are returned as a JSON array , not a scalar. Microsoft states that JSON_QUERY extracts an object or array from JSON text, which is exactly the right behavior for an embeddings payload.

The unused options are not the best fit here:

OPENJSON is mainly for shredding JSON into rows and columns.

AI_GENERATE_CHUNKS is for chunking text, not extracting fields from a response payload.

VECTOR_DISTANCE computes similarity between vectors and is unrelated to JSON extraction.

FOR XML PATH produces XML, not JSON.

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 insertion at line 22 is FOR JSON PATH, WITHOUT_ARRAY_WRAPPER .

The request body for the Azure OpenAI chat completions call must be a single JSON object containing the messages array with both the system/user content and the retrieved chunks. Microsoft documents that FOR JSON PATH is the preferred way to shape JSON output, especially when you want precise control over nested property names like messages[0].role and messages[1] .content.

The key detail is WITHOUT_ARRAY_WRAPPER . By default, FOR JSON returns results enclosed in square brackets as a JSON array. Microsoft documents that WITHOUT_ARRAY_WRAPPER removes those brackets so a single JSON object is produced instead. That is exactly what is needed here for @payload, because the stored procedure is building one request body, not an array of request bodies.

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 .

For an Azure App Service using a user-assigned managed identity to connect to Azure SQL Database with Microsoft Entra authentication , the required database-side step is to create a database user from the external provider , then grant the needed database roles. Microsoft’s Azure SQL documentation for managed identities states that to let a managed identity access the target database, you create a SQL user for that identity by using:

CREATE USER [ < identity-name > ] FROM EXTERNAL PROVIDER;

and then assign the appropriate roles.

That makes db_datareader and db_datawriter the right role grants here, because the requirement says the identity must have read and write permissions to SalesDB.

The other options are incorrect:

A uses CREATE LOGIN ... FROM EXTERNAL PROVIDER, which is not the right choice for this Azure SQL Database scenario; the documented pattern is to create a database user from the external provider.

B and D create SQL-authentication principals with passwords, which does not meet the Microsoft Entra managed-identity requirement.

D also grants sysadmin, which is a server-level overgrant and not appropriate for the stated read/write requirement.

The best explanation is an open explicit transaction . During the incident, session 72 was sleeping but still had open_transaction_count = 1 , and sys.dm_exec_input_buffer(72, NULL) showed only BEGIN TRANSACTION UPDATE Sales.Orders. That pattern indicates the session executed an update inside an explicit transaction and then remained idle without committing or rolling back , while still holding locks. Other sessions showing blocking_session_id = 72 is the expected symptom of that situation. Microsoft explains that blocking occurs when one session holds a lock on a resource and another session requests a conflicting lock, and sleeping sessions can continue to block if they retain locks through an open transaction.

This also fits the observed behavior that the timeouts stopped only after session 72 was terminated . Killing the session would roll back the active transaction and release the locks, allowing waiting updates to continue. That is much more consistent with an uncommitted transaction than with a deadlock, because deadlocks are normally detected and one session is chosen as the victim automatically rather than persisting until manual termination.

The correct way to avoid full table scans here is to add a computed column that extracts the JSON scalar property with JSON_VALUE , and then create a nonclustered index on that computed column with the report’s returned columns in the INCLUDE list. Microsoft’s JSON indexing guidance specifically recommends creating a computed column that exposes the JSON property you filter on, using the same expression as in the query, and then indexing that computed column.

So the computed column must be:

AS JSON_VALUE([log], ' $.severity ' ) PERSISTED

This is correct because $.severity is a scalar JSON value, so JSON_VALUE is the proper function. JSON_QUERY would be for extracting an object or array, not a scalar property. Microsoft also notes that persisted computed columns can improve access speed for JSON-derived values.

The index should then include:

INCLUDE (LogId, LogDateTime, [log])

That is the right covering strategy because the report filters by severity but returns LogId, LogDateTime, and log. Microsoft’s guidance on included columns explains that nonkey included columns let a nonclustered index cover more queries and reduce extra lookups to the base table.

So the completed code is:

ALTER TABLE WebSite.Logs

ADD severity AS JSON_VALUE([log], ' $.severity ' ) PERSISTED;

GO

CREATE INDEX ix_severity

ON WebSite.Logs(severity)

INCLUDE (LogId, LogDateTime, [log]);

GO

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