The answer is Azure Event Hubs -> Azure Stream Analytics -> Azure Cosmos DB. This combination is correct because Azure Stream Analytics natively supports low-latency, windowed aggregation—such as a TumblingWindow for per-minute calculations—directly on the IoT telemetry stream ingested from Event Hubs, and it writes the aggregated results to Cosmos DB through a built-in sink, eliminating the need for an intermediate compute or storage layer and minimizing end-to-end latency. On the DP-203 exam, this scenario tests your understanding of near-real-time data processing patterns and the native integration between Azure services; a common trap is adding an unnecessary Azure Data Lake Storage or Azure Databricks step, which would increase latency and complexity. Remember the mnemonic “E-S-C” (Event Hubs, Stream Analytics, Cosmos DB) to recall the direct pipeline for streaming aggregation without extra hops.
DP-203 Design and develop data processing Practice Question
This DP-203 practice question tests your understanding of design and develop data processing. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
You need to design a near-real-time data processing solution that ingests IoT telemetry data from millions of devices. The data must be aggregated per minute and stored in Azure Cosmos DB for low-latency queries. Which Azure service combination should you use?
Option B is correct because Azure Stream Analytics provides native, low-latency windowed aggregation (e.g., TumblingWindow for per-minute aggregates) directly on data ingested from Event Hubs, and it has a built-in output sink to Azure Cosmos DB. This combination meets the near-real-time requirement without needing an intermediate compute or storage layer, minimizing end-to-end latency.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
Databricks streaming can work but is less straightforward.
✗
Azure Event Hubs -> Azure Data Factory -> Azure Cosmos DB
Why it's wrong here
Data Factory is not real-time.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often over-engineer the solution by adding a big-data processing layer (like HDInsight or Databricks) when a simpler, fully managed stream analytics service (Azure Stream Analytics) is the correct choice for fixed-window aggregation and direct Cosmos DB output.
Trap categories for this question
Scenario analysis trap
Overengineered for this scenario.
Detailed technical explanation
How to think about this question
Azure Stream Analytics uses a SQL-like query language with built-in temporal operators (e.g., TumblingWindow, HoppingWindow) that operate on the event stream in-memory, achieving sub-second processing latency. The direct output to Cosmos DB uses the Change Feed to enable real-time queries, and the service automatically scales partitions based on Event Hubs throughput units. In a real-world scenario, millions of devices sending 1 KB messages per second would require careful partition key design in Cosmos DB (e.g., deviceId) to avoid hot partitions and ensure consistent write throughput.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this DP-203 question in full detail.
Design and develop data processing — This question tests Design and develop data processing — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Azure Event Hubs -> Azure Stream Analytics -> Azure Cosmos DB — Option B is correct because Azure Stream Analytics provides native, low-latency windowed aggregation (e.g., TumblingWindow for per-minute aggregates) directly on data ingested from Event Hubs, and it has a built-in output sink to Azure Cosmos DB. This combination meets the near-real-time requirement without needing an intermediate compute or storage layer, minimizing end-to-end latency.
What should I do if I get this DP-203 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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