Because the SQL Server 2025 instance has no outbound network connectivity , the embedding model cannot rely on a remote REST endpoint such as Azure AI Foundry or Azure OpenAI. Microsoft’s CREATE EXTERNAL MODEL documentation includes a local deployment pattern using ONNX Runtime running locally with local runtime/model paths . That is the right design when embeddings must be generated inside the SQL Server instance without external network access. Microsoft explicitly documents a local ONNX Runtime example for SQL Server 2025 and notes the required local runtime setup and model path configuration.

The permission requirement is handled by granting the application user access to use the external embeddings model. Microsoft’s AI_GENERATE_EMBEDDINGS documentation states that, as a prerequisite, you must create an external model of type EMBEDDINGS that is accessible via the correct grants, roles, and/or permissions . Among the choices, the exam-appropriate action is to grant execute permission on the external model project to AlApplicationUser so only that database user can run embedding generation through the model.

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

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

For an SDK-style SQL database project targeting Azure SQL Database , Microsoft recommends using the Azure SQL system DACPAC as a NuGet package reference rather than adding a direct artifact reference to master.dacpac for new SDK-style development. Microsoft’s SQL Database Projects documentation says direct .dacpac artifact references are not recommended for new development in SDK-style projects ; instead, use NuGet package references .

Because the goal is specifically to make dotnet build validate successfully with the correct Azure SQL system objects , adding an artifact reference to master.dacpac is not the recommended SDK-style solution. It can work in some project styles, but it does not meet the stated goal as the proper approach for SDK-style Azure SQL projects.

The correct drag-and-drop mapping is based on the documented behavior of the new SQL regular expression functions.

For RawNumber , the requirement is to return the first substring in MessageRaw that matches the phone-number pattern, or NULL if nothing matches. That is exactly what REGEXP_SUBSTR does: it extracts the matched substring from the source text. Microsoft documents REGEXP_SUBSTR as the function that “extracts parts of a string based on a regular expression pattern” and returns the matched occurrence.

For DigitsOnly , you first need the matched phone substring, then remove all non-digit characters from it. The correct combined expression is REGEXP_REPLACE( REGEXP_SUBSTR( so the matched substring is passed into REGEXP_REPLACE, which strips characters matching \D. Microsoft documents REGEXP_REPLACE as returning a modified source string with matching patterns replaced. Using it around REGEXP_SUBSTR satisfies the “digits only or NULL” requirement in one select expression.

For PhoneStatus , the requirement is simply to return Valid when a phone number exists and Missing otherwise. That is a Boolean test, so REGEXP_LIKE is the right function. Microsoft documents REGEXP_LIKE as returning a Boolean value indicating whether the input matches the regex pattern.

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 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 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.