Microsoft Updated DP-800 Exam Questions and Answers by neve

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 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.

Microsoft documents that DriftReport creates an XML report showing changes that have been made to the registered database since it was last registered . That is the action intended to detect whether the production schema has diverged from the expected model baseline in your deployment workflow.

This is different from DeployReport , which shows the changes that would be made by a publish action . In other words:

DriftReport answers: Has the deployed database drifted from the registered state/model?

DeployReport answers: What changes would be applied if I published now?

The other options are not the right fit:

Extract creates a DACPAC from an existing database, not a drift analysis report.

Script generates a deployment script, not a schema-drift report.

So to generate a report that shows whether production has diverged from the model in source control , add:

SqlPackage.exe /Action:DriftReport

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