DP-203 Develop data processing Practice Question
You are building a real-time dashboard to monitor user activity on a website. The data is ingested via Azure Event Hubs and must be aggregated every minute with a 30-second late-arrival tolerance. The aggregated results should be stored in Azure Cosmos DB for low-latency reads. Which Azure service should you use to perform the windowed aggregation?
⚠ Common exam trap
Candidates often confuse tumbling windows (fixed, non-overlapping) with sliding windows (continuous, overlapping) or assume that any compute service (like Functions or Databricks) can easily replicate Stream Analytics' built-in windowing and late-arrival handling, ignoring the complexity of state management and exactly-once semantics.
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
✓
Azure Stream Analytics with a tumbling window of 1 minute and a late-arrival policy of 30 seconds.
Azure Stream Analytics is the correct choice because it natively supports windowed aggregations (tumbling, hopping, sliding, session) and allows you to define a late-arrival policy to handle out-of-order events. A tumbling window of 1 minute with a late-arrival tolerance of 30 seconds meets the requirement exactly, and the output can be directly written to Azure Cosmos DB for low-latency reads.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Azure Stream Analytics with a tumbling window of 1 minute and a late-arrival policy of 30 seconds.
Why this is correct
Azure Stream Analytics natively supports tumbling windows, producing non-overlapping one-minute aggregates, and its event-ordering late-arrival tolerance of 30 seconds holds back results until stragglers arrive. This satisfies both the one-minute aggregation cadence and the 30-second tolerance, while writing output directly to Cosmos DB.
- ✗
Azure Functions triggered by Event Hubs to aggregate data and write to Cosmos DB.
Why it's wrong here
Event Hubs-triggered Functions execute per batch without native windowing state, so minute-level tumbling windows with a 30-second late-arrival tolerance must be hand-coded and are not guaranteed. It is tempting because Functions are serverless and event-driven, and it would be correct for lightweight per-event processing.
- ✗
Azure Databricks with structured streaming and a sliding window.
Why it's wrong here
Databricks structured streaming sliding windows process on micro-batch triggers, and its watermarking is designed for event-time lateness handling rather than the sub-minute, 30-second tolerance required here. It is tempting because it handles complex stateful streaming, and it would be correct for large-scale transformation pipelines.
- ✗
Azure Analysis Services to process streaming data directly from Event Hubs.
Why it's wrong here
Azure Analysis Services serves tabular models over batch-loaded data, not direct Event Hubs ingestion, so it cannot apply the one-minute tumbling window with 30-second late-arrival tolerance. It is tempting because it delivers fast analytical queries, and would suit serving pre-aggregated semantic models to BI tools rather than stream processing.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.