Microsoft Updated DP-800 Exam Questions and Answers by neve

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 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 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.