The correct answer is D. The inference of incrementally processed records as soon as a trigger is hit. In this context, a “trigger” refers to the condition that initiates the processing of the next set of data. This could be a time interval (e.g., process new data every second), a data size (e.g., process every 1000 records), or other custom conditions

Streaming with Spark as a model deployment strategy involves processing data in small, incremental batches (micro-batches) as it arrives. Spark Structured Streaming allows for continuous processing of streaming data, where the data is processed incrementally and results are updated in real-time. The processing is typically triggered at regular intervals, known as trigger

Incrementally processed records with a Spark job: Spark jobs are typically used for initiating processing, but triggers are more common for continuous inference in streaming scenarios.