Question 783 of 760
DP-203 Develop data processing Practice Question
You are designing a data processing solution in Azure Databricks to transform streaming data from Azure Event Hubs. The data must be aggregated in 1-minute tumbling windows and written to Azure Synapse Analytics. Which Spark API should you use?
⚠ Common exam trap
It's easy for candidates to confuse the older Spark Streaming (DStreams) API with Structured Streaming, assuming both are equally capable for event-time windows, but DStreams lack native event-time support and are deprecated in favor of Structured Streaming.
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
✓
Structured Streaming
Structured Streaming is the correct choice because it provides native support for event-time-based aggregations, such as 1-minute tumbling windows, and integrates seamlessly with Azure Event Hubs as a streaming source and Azure Synapse Analytics as a streaming sink using the `foreachBatch` or `writeStream` API. It offers exactly-once semantics and automatic state management for windowed operations, which are essential for reliable streaming ETL.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
RDD API
Why it's wrong here
Low-level, not suitable for streaming.
- ✓
Structured Streaming
Why this is correct
Supports windowed aggregations and streaming sinks.
- ✗
Spark Streaming (DStreams)
Why it's wrong here
Legacy API; Structured Streaming preferred.
- ✗
DataFrame API with batch processing
Why it's wrong here
Not for streaming.
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Last reviewed: Jun 24, 2026
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