You are building a solution that uses a multi-partition Azure Cosmos DB database and the latest Cosmos DB SDK. Requirements: send insert and update operations to Azure Blob Storage, process changes from all partitions immediately, and allow parallel processing of change handling. Which two approaches will let you process the Cosmos DB operations? (Each correct answer is a complete solution.)

The Azure Cosmos DB trigger for Azure Functions is a managed solution that automatically processes changes from the change feed, handles partitioning, and scales horizontally. This meets the requirements for immediate and parallel processing across all partitions. Alternatively, using a FeedIterator directly with FeedRange allows for manual control over change feed processing. You can explicitly define ranges to be processed by different instances of an Azure Function, enabling parallel processing and immediate handling of changes. Deploying an Azure App Service API with the change feed estimator is less efficient for immediate, parallel processing compared to Azure Functions, as it requires more manual scaling and management of the change feed cursor. Running a background job in AKS using the SDK's change feed functionality is also a viable option but requires more operational overhead for managing the Kubernetes cluster and ensuring proper scaling and fault tolerance compared to the serverless Azure Functions approach.