Contoso has an Azure subscription in North Europe that contains the corporate infrastructure. The current infrastructure contains a Microsoft SQL Server 2017 database. The database contains the following tables.
The FeedbackJson column has a full-text index and stores JSON documents in the following format.
The support staff at Contoso never has the unmask permission.
Requirements
Contoso is deploying a new Azure SQL database that will become the authoritative data store for the following;
Sometimesthe ingestion pipeline fails due to malformed JSON and duplicate payloads.
The engineers at Contoso report that the following dashboard query runs slowly.
SELECT VehicleTd, Lastupdatedutc, EngineStatus, BatteryHealth FROM dbo.VehicleHealthSumary where fleetld- gFleetld ORDER BV LastUpdatedUtc DESC;
You review the execution plan and discover that the plan shows a clustered index scan.
vehicleincidentReports often contains details about the weather, traffic conditions, and location. Analysts report that it is difficult to find similar incidents based on these details
Planned Changes
Contoso wants to modernize Fleet Intelligence Platform to support Al-powered semantic search over
incident reports.
Security Requirements
Contoso identifies the following telemetry requirements:
• Telemetry data must be stored in a partitioned table.
• Telemetry data must provide predictable performance for ingestion and retention operations.
• latitude, longitude, and accuracy JSON properties must be filtered by using an index seek.
Contoso identifies the following maintenance data requirements:
• Ensure that any changes to a row in the MaintenanceEvents table updates the corresponding
value in the LastModif reduce column to the time of the change.
• Avoidrecursive updates.
AI Search, Embedding’s, and Vector indexing
The development learn at Contoso will use Microsoft Visual Studio Code and GitHub Copilot and will
retrieve live metadata from the databases. Contoso identifies the following requirements for
querying data in the FeedbackJson column of the customer-Feedback table:
• Extract the customer feedback text from the JSON document.
• Filter rows where the JSON text contains a keyword.
• Calculate a fuzzy similarity score between the feedback text and a known issue description.
• Orderthe results by similarity score, with the highest score first
View Mode
Q: 7
HOTSPOT You need to meet the development requirements for the FeedbackJson column How should you complete the Transact SQL query? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.
Your Answer
Discussion
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Correct Answer:
DROPDOWN 1:JSON_VALUE(F.FEEDBACKJSON, '$.TEXT') AS FEEDBACKTEXT
DROPDOWN 2:CONTAINS(FEEDBACKJSON, @KEYWORD)
DROPDOWN 3:SIMILARITYSCORE
Explanation
Dropdown 1: The JSON_VALUE function is specifically required here to extract a scalar string value from the JSON property $.text. The alternative option, JSON_QUERY, is incorrect because it is designed to extract entire JSON objects or arrays, not scalar text.
Dropdown 2: The CONTAINS full-text search predicate is used in the WHERE clause to perform an initial, highly efficient filter for rows containing the @keyword. This optimization pattern ensures that the compute-heavy EDIT_DISTANCE function is only executed on a narrowed dataset, rather than running a table scan.
Dropdown 3: The alias SimilarityScore is computed in the SELECT statement. Due to T-SQL logical query processing phases, aliases created in the SELECT list cannot be evaluated as expressions in the WHERE clause. However, they can be validly passed to an ORDER BY clause to sort the final output by the computed fuzzy match distance.
