DP-203 Develop data processing Practice Question
You are developing an Azure Databricks notebook that processes streaming data from Azure Event Hubs and writes to a Delta Lake table. The stream must handle late-arriving data up to 30 minutes old and ensure that aggregations are computed correctly even if events arrive out of order. You need to minimize state store size and avoid unbounded growth. Which combination of features should you use?
⚠ Common exam trap
The trap here is assuming that a retention duration can be set manually for the state store, or that complete output mode is suitable for late data; actually, the watermark controls state cleanup.
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 event-time processing with a watermark of 30 minutes and apply a windowed aggregation with a tumbling window of 10 minutes.
Using event-time processing with a watermark of 30 minutes ensures that late-arriving data up to 30 minutes old is processed correctly. A tumbling window of 10 minutes defines the aggregation intervals, and the watermark automatically cleans up state for windows that are older than the watermark, preventing unbounded state growth. This approach balances correctness and resource usage.
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 with a 30-minute watermark and configure the state store with a retention duration of 30 minutes.
Why it's wrong here
A tumbling window with a watermark handles late data, but the state store retention duration is not a parameter you configure directly in Structured Streaming. The watermark defines how long state is kept, and setting it to 30 minutes will drop late data beyond that. However, the state store cleanup is automatic based on the watermark, so specifying a retention duration is not a valid configuration.
- ✗
Use event-time processing with a watermark of 30 minutes and apply a 'dropDuplicates' operation on the event ID within the watermark.
Why it's wrong here
Event-time processing with a watermark is correct for handling late data, but dropDuplicates on event ID within the watermark does not directly address aggregation correctness or state store size. Deduplication can help with duplicate events, but the primary requirement is to compute aggregations correctly with late data, which is achieved through watermarking and windowing, not deduplication alone.
- ✓
Use event-time processing with a watermark of 30 minutes and apply a windowed aggregation with a tumbling window of 10 minutes.
Why this is correct
Event-time processing with a watermark of 30 minutes allows late data up to 30 minutes old to be included in the correct window. A tumbling window of 10 minutes defines the aggregation intervals. The watermark ensures that state for windows older than 30 minutes is cleaned up, preventing unbounded state growth. This combination correctly handles late data and manages state size.
- ✗
Use a sliding window with a 30-minute watermark and set the output mode to 'complete'.
Why it's wrong here
A sliding window with a 30-minute watermark can handle late data, but using 'complete' output mode requires storing all state indefinitely, which leads to unbounded state growth. This contradicts the requirement to minimize state store size. Complete mode is typically used for aggregations that need to output the entire result set on each trigger, which is not suitable for streaming with late data.
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JA
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
This DP-203 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DP-203 exam.