DP-203 Develop data processing Practice Question
You are building an Azure Stream Analytics job that reads from an Azure Event Hubs input and writes to an Azure Synapse Analytics dedicated SQL pool. You need to compute a 5-minute tumbling window aggregation that outputs only once per window after all events for that window have arrived. Which query construct should you use?
⚠ Common exam trap
Many candidates confuse tumbling windows with hopping windows, which overlap and emit multiple results per interval.
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
✓
GROUP BY with TUMBLINGWINDOW and a 5-minute window, emitting results when the window closes
Tumbling windows are fixed-size, non-overlapping, and emit a single result when the window closes, which aligns exactly with the need to output once per 5-minute interval after all events arrive. Other window types or external scheduling mechanisms either emit too frequently or cannot guarantee completeness of the window before output.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
A self-join on the input stream using DATEDIFF to group events into 5-minute buckets
Why it's wrong here
A self-join with DATEDIFF can approximate bucketing, but it does not provide window semantics, watermark handling, or exactly-once emission per window. It is error-prone and does not guarantee that all late events for a window are included before output. Stream Analytics provides dedicated windowing functions for this purpose.
- ✓
GROUP BY with TUMBLINGWINDOW and a 5-minute window, emitting results when the window closes
Why this is correct
TUMBLINGWINDOW in Azure Stream Analytics defines fixed-size, non-overlapping, contiguous time intervals. When the window closes, the job emits a single aggregated result per window. This matches the requirement to output only once per 5-minute window after all events are processed, and it is the native construct for this pattern in Stream Analytics.
- ✗
A persistent SQL table in the dedicated SQL pool that stores events, with a scheduled stored procedure running every 5 minutes
Why it's wrong here
This approach introduces a separate scheduling mechanism and relies on the dedicated SQL pool to perform windowing, which is not the responsibility of the sink. It also cannot guarantee that all streaming events have arrived before the stored procedure runs. The requirement is best met within the Stream Analytics query itself.
- ✗
GROUP BY with HOPPINGWINDOW and a 5-minute window and 1-minute hop
Why it's wrong here
HOPPINGWINDOW creates overlapping windows that emit results more frequently than once per five minutes. With a 1-minute hop, you would get outputs every minute, violating the requirement to output only once per window. Hopping windows are for sliding aggregations, not for single-emission tumbling calculations.
Go deeper
Related to this question
Learn chapter
Develop Stream Processing Solutions
Key term
Azure Stream Analytics
Azure Stream Analytics is a fully managed, real-time data processing service that analyzes and transforms high volumes of streaming data from various sources to deliver low-latency insights and trigger actions.
Key term
Azure Synapse Analytics
Azure Synapse Analytics is a cloud-based data integration, warehousing, and analytics service that brings together big data and data warehouse capabilities under one platform.
About these practice questions
This DP-203 question is part of Courseiva's 509-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
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.