This DP-900 practice question tests your understanding of describe core data concepts. 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.
A data engineer needs to process streaming data from IoT devices in near real-time and store the results in Azure Cosmos DB. Which Azure service should they use for the stream processing?
Azure Synapse Analytics
Why wrong: Synapse is for large-scale analytics, not lightweight streaming.
Azure Databricks
Why wrong: Databricks can process streaming but requires more setup; Stream Analytics is simpler.
Azure Stream Analytics
Stream Analytics provides near real-time stream processing with native Cosmos DB sink.
Azure Data Factory
Why wrong: ADF is for batch data movement and orchestration, not real-time streaming.
Answer choices
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
Azure Stream Analytics is the correct choice because it is a fully managed, real-time stream processing engine designed specifically for low-latency, near-real-time analytics on streaming data. It can ingest data from IoT devices via Event Hubs or IoT Hub, apply SQL-based transformations, and directly output the results to Azure Cosmos DB with millisecond latency, making it ideal for this scenario.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Azure Synapse Analytics
Why it's wrong here
Synapse is for large-scale analytics, not lightweight streaming.
Azure Databricks
Why it's wrong here
Databricks can process streaming but requires more setup; Stream Analytics is simpler.
Azure Stream Analytics
Why this is correct
Stream Analytics provides near real-time stream processing with native Cosmos DB sink.
Related concept
Read the scenario before looking for a memorised answer.
Azure Data Factory
Why it's wrong here
ADF is for batch data movement and orchestration, not real-time streaming.
Common exam traps
The trap here is that candidates often confuse Azure Stream Analytics with Azure Data Factory or Azure Databricks, mistakenly thinking that any 'data processing' tool can handle real-time streaming, but only Stream Analytics is purpose-built for near-real-time, serverless stream processing with direct Cosmos DB integration.
Detailed technical explanation
Under the hood, Azure Stream Analytics uses a temporal windowing engine (e.g., tumbling, hopping, or sliding windows) to aggregate streaming data in memory before writing to Cosmos DB, ensuring exactly-once semantics for output. A real-world scenario is processing temperature sensor data from thousands of IoT devices: Stream Analytics can filter anomalies, compute rolling averages, and store results in Cosmos DB with a throughput of up to 1 GB/second per streaming unit, while handling out-of-order events via a configurable late-arrival policy.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
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FAQ
Describe core data concepts — This question tests Describe core data concepts — Read the scenario before looking for a memorised answer..
The correct answer is: Azure Stream Analytics — Azure Stream Analytics is the correct choice because it is a fully managed, real-time stream processing engine designed specifically for low-latency, near-real-time analytics on streaming data. It can ingest data from IoT devices via Event Hubs or IoT Hub, apply SQL-based transformations, and directly output the results to Azure Cosmos DB with millisecond latency, making it ideal for this scenario.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
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