Answer: storage event

## Explanation To configure an Azure Data Factory pipeline to run when a file is deleted from Azure Blob Storage with minimal development effort, the **Storage Event trigger** is the optimal choice. ### Why Storage Event Trigger (Option B) is Correct: - **Native Integration**: Azure Data Factory provides built-in Storage Event triggers that can monitor Azure Blob Storage containers for specific events, including file creation, modification, and deletion. - **Event-Driven Architecture**: This trigger type is specifically designed to respond to Azure Storage events, making it perfectly suited for scenarios where pipeline execution should be triggered by file operations like deletion. - **Minimal Development Effort**: The Storage Event trigger requires minimal configuration - you simply specify the storage account, container, and the type of event (in this case, file deletion). No custom code or complex setup is needed. - **Real-time Response**: Storage Event triggers provide near real-time response to file deletion events, ensuring the pipeline runs promptly when the specified condition occurs. ### Why Other Options Are Less Suitable: - **Schedule Trigger (Option A)**: This trigger runs pipelines on a fixed schedule (hourly, daily, etc.), not in response to specific file deletion events. It would require polling and additional logic to detect deletions, increasing development complexity. - **Tumbling Window Trigger (Option C)**: This is designed for time-based windowing scenarios where pipelines need to process data in fixed, non-overlapping time intervals. It's not event-driven and doesn't respond to file operations. - **Custom Event Trigger (Option D)**: While this could be configured to handle file deletion events, it would require significantly more development effort, including setting up Azure Event Grid custom topics and custom event publishing logic. ### Best Practice Consideration: For event-driven scenarios involving Azure Storage operations, Storage Event triggers represent the most efficient and Azure-native approach, aligning with Azure Data Factory's integration capabilities and minimizing custom development overhead.

Author: LeetQuiz Editorial Team