DP-203 Develop data processing Practice Question
You are implementing a Spark Structured Streaming job in Azure Databricks that reads from an Azure Event Hubs topic and writes to a Delta table. The job must handle late-arriving data up to 10 minutes and aggregate counts per device every 5 minutes. Which combination of settings should you use?
⚠ Common exam trap
The trap here is mixing up the window duration with the watermark duration; the watermark must be at least as long as the maximum expected late arrival, while the window defines the aggregation period.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use a tumbling window of 5 minutes and set watermark to 10 minutes on the event timestamp.
A tumbling window of 5 minutes with a 10-minute watermark on the event timestamp satisfies both the aggregation interval and the late-data tolerance. The tumbling window ensures non-overlapping 5-minute counts, and the watermark allows events up to 10 minutes late to be included. Other options use incorrect window sizes, slide intervals, or watermark durations that fail the requirements.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use a tumbling window of 5 minutes and set watermark to 10 minutes on the event timestamp.
Why this is correct
A tumbling window of 5 minutes creates non-overlapping aggregation intervals, and a watermark of 10 minutes allows late data up to that delay to be included in the correct window. This matches the requirement for 5-minute counts and tolerance for 10-minute late arrivals. Watermarking also enables state cleanup for long-running streams, preventing unbounded state growth.
- ✗
Use a sliding window of 5 minutes with a 10-minute slide interval and set watermark to 5 minutes.
Why it's wrong here
A sliding window with a 5-minute window and 10-minute slide produces overlapping windows every 10 minutes, which does not yield counts every 5 minutes. The watermark of 5 minutes is also insufficient for 10-minute late data. This configuration would miss late events beyond 5 minutes and produce aggregations at the wrong cadence, failing both requirements.
- ✗
Use a tumbling window of 10 minutes and set watermark to 5 minutes.
Why it's wrong here
A 10-minute tumbling window aggregates counts every 10 minutes, not every 5 minutes as required. A 5-minute watermark allows only 5 minutes of late data, so events arriving 6 to 10 minutes late would be dropped. Both the window size and the watermark duration are incorrect for the stated latency and aggregation interval.
- ✗
Use a hopping window of 5 minutes with a 5-minute hop and set watermark to 10 minutes.
Why it's wrong here
A hopping window with equal window and hop size is effectively a tumbling window, but the terminology is misleading. More importantly, the requirement is for non-overlapping 5-minute counts, which a tumbling window expresses directly. While the watermark of 10 minutes is correct, using a hopping window with a 5-minute hop produces the same result but is not the canonical choice and may confuse maintenance.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Microsoft exam blueprint
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