DP-203 Develop data processing Practice Question
You are designing a real-time analytics solution for IoT devices that emit telemetry data every second. The data must be aggregated every minute and stored in Azure SQL Database for historical analysis. You need to minimize latency and operational overhead. Which approach should you recommend?
⚠ Common exam trap
The trap here is that candidates often over-engineer the solution by choosing Databricks (Option A) for its flexibility, overlooking that Stream Analytics is purpose-built for low-latency, windowed aggregations with minimal operational overhead, while Databricks adds unnecessary complexity for simple time-based aggregations.
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 Azure Stream Analytics with a tumbling window of 1 minute and output to Azure SQL Database
Azure Stream Analytics natively supports real-time stream processing with tumbling windows, allowing you to aggregate IoT telemetry data every minute and output directly to Azure SQL Database with minimal latency. This approach avoids the overhead of managing clusters (Databricks) or orchestrating batch loads (Data Factory), directly meeting the requirement for low latency and operational simplicity.
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 Azure Databricks with Structured Streaming to aggregate and write to SQL Database
Why it's wrong here
Databricks Structured Streaming requires provisioning and tuning clusters, adding operational overhead the stem asks to minimise. It suits complex stateful transformations and machine learning pipelines. Stream Analytics runs the minute-window aggregation serverlessly and writes directly to Azure SQL Database.
- ✗
Use Event Hubs Capture to store raw data in blob storage, then use Azure Data Factory to load into SQL Database hourly
Why it's wrong here
Event Hubs Capture writes raw data to blob storage, and hourly Data Factory loads breach the one-minute aggregation requirement. This pattern suits batch archival and cold-path analytics. Stream Analytics aggregates in-stream each minute and writes directly to Azure SQL Database.
- ✓
Use Azure Stream Analytics with a tumbling window of 1 minute and output to Azure SQL Database
Why this is correct
A tumbling window aggregates each minute's one-second telemetry into a single row, cutting write volume and latency while satisfying the one-minute aggregation requirement. Stream Analytics is fully managed, so operational overhead stays low, and its native Azure SQL Database output sinks results directly for historical analysis.
- ✗
Use Azure Functions to process events and write to SQL Database
Why it's wrong here
Azure Functions lacks built-in windowing, so minute-level tumbling aggregates must be hand-coded with external state, adding latency and complexity. Functions suit lightweight per-event processing. Stream Analytics provides native tumbling windows and a SQL Database output sink.
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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.