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In Structured Streaming, a watermark defines the maximum delay for event-time data to be considered in stateful operations like deduplication or window aggregations.

Behavior:

df = df.withWatermark("event_timestamp", "30 minutes")

This sets a 30-minute watermark, meaning Spark will only keep track of events that arrive within 30 minutes of the latest event time seen so far. When used with:

df.dropDuplicates(["event_id", "event_timestamp"])

Spark removes duplicates that arrive within the watermark threshold (in this case, within 30 minutes).

Why other options are incorrect:

A: Watermarks do not remove all duplicates; they only manage those within the defined event-time window.

B: Watermark durations can be expressed as strings like "30 minutes", "10 seconds", etc., not only seconds.

D: Structured Streaming supports deduplication using withWatermark() and dropDuplicates().

References (Databricks Apache Spark 3.5 – Python / Study Guide):

PySpark Structured Streaming Guide — withWatermark() and dropDuplicates() methods for event-time deduplication.

Databricks Certified Associate Developer for Apache Spark Exam Guide (June 2025): Section “Structured Streaming” — Topic: Streaming Deduplication with and without watermark usage.