Existing Environment
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;
1. Al workloads
2. Vector search
3. Modernized API access
4. Retrieval Augmented Generation (RAG) pipelines
Sometimes the 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:
1. Telemetry data must be stored in a partitioned table.
2. Telemetry data must provide predictable performance for ingestion and retention operations.
3. latitude, longitude, and accuracy JSON properties must be filtered by using an index seek.
Contoso identifies the following maintenance data requirements:
1. 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.
2. Avoid recursive 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:
1. Extract the customer feedback text from the JSON document.
2. Filter rows where the JSON text contains a keyword.
3. Calculate a fuzzy similarity score between the feedback text and a known issue description.
4. Order the results by similarity score, with the highest score first.