Answer: trigger(availableNow=True)

## Explanation In Apache Spark Structured Streaming, the `trigger()` method controls when the streaming query executes micro-batches. The `availableNow=True` trigger option is specifically designed for processing all available data in multiple batches and then terminating the query. ### Why Option B is correct: 1. **`trigger(availableNow=True)`** - This trigger processes all available data at the start of the query in one or more micro-batches, then stops the query. It's ideal for scenarios where you want to process all existing data without running a continuous streaming job. ### Why other options are incorrect: - **A. `processingTime(1)`** - This would create a continuous streaming job that runs every 1 second, not just process all available data once. - **C. `trigger(parallelBatch=True)`** - This is not a valid trigger option in Spark Structured Streaming. - **D. `trigger(processingTime="once")`** - This is not a valid syntax; `processingTime` expects a time interval string like "1 second" or a number of seconds. - **E. `trigger(continuous="once")`** - This is not a valid trigger option; continuous mode requires a checkpoint interval. ### Key Points: - `availableNow=True` is designed for batch-like processing of all available data - It processes data in multiple micro-batches to avoid overwhelming the system - The query automatically stops after processing all data - This is different from `once=True` (deprecated) which processed all data in a single batch - This trigger is particularly useful for incremental data processing scenarios where you want to process all new data since the last run

Author: Keng Suppaseth