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How can you ensure accurate results in a Spark Structured Streaming job that processes time-windowed aggregates when dealing with late-arriving data?
A
Increase the state timeout duration to an arbitrarily high value to account for all possible late data.
B
Manually adjust the system clock to account for data latency before processing each micro-batch.
C
Use the watermark feature to specify a threshold for late data and update window aggregates accordingly.
D
Ignore late data, focusing only on data arriving within the expected time window.
