What command is typically used to keep track of data ingestion in a streaming scenario in Databricks?

In a streaming scenario within Databricks, the command that is typically used to handle data ingestion effectively is associated with managing state in the context of streaming queries. The correct choice incorporates the concept of watermarks, which are essential for handling late-arriving data and managing stateful aggregations over time.

Using the command that involves watermarks allows you to set a threshold for how long the streaming engine should keep historical state data available for processing. This is particularly crucial because, in stream processing, data can arrive out of sequence, and watermarks help define when the system can safely discard data that is no longer relevant to ongoing computations. This aids in optimizing resource usage, improving performance, and ensuring the system operates efficiently without retaining unnecessary data indefinitely.

The other options, while they may seem plausible, do not specifically address the requirements of managing state and late data arrivals in streaming applications like watermarks do. Therefore, the command associated with watermarks is the most appropriate and effective choice for tracking data ingestion in a streaming context in Databricks.