This DP-900 practice question tests your understanding of describe an analytics workload on azure. 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 company needs to analyze streaming data from IoT devices in real time. They want to identify anomalies and trigger alerts. Which Azure service should they use as the core processing engine?
Azure Stream Analytics
Stream Analytics is built for real-time data processing and alerting.
Azure Synapse Analytics
Why wrong: Synapse is for analytics, but not primarily for real-time streaming.
Azure Databricks
Why wrong: Databricks can process streaming but is more complex; Stream Analytics is simpler.
Azure Data Lake Storage
Why wrong: Data Lake is for storage, not real-time processing.
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 purpose-built for real-time stream processing, allowing you to define SQL-like queries that run continuously against streaming data from sources like IoT Hub. It can detect anomalies and trigger alerts on the fly, making it the correct core processing engine for this IoT 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 Stream Analytics
Why this is correct
Stream Analytics is built for real-time data processing and alerting.
Related concept
Read the scenario before looking for a memorised answer.
Azure Synapse Analytics
Why it's wrong here
Synapse is for analytics, but not primarily for real-time streaming.
Azure Databricks
Why it's wrong here
Databricks can process streaming but is more complex; Stream Analytics is simpler.
Azure Data Lake Storage
Why it's wrong here
Data Lake is for storage, not real-time processing.
Common exam traps
The trap here is that candidates often confuse batch processing tools like Synapse Analytics or Databricks with real-time stream processing, overlooking that only Azure Stream Analytics is designed as a dedicated, low-latency stream processing engine for this exact pattern.
Detailed technical explanation
Under the hood, Azure Stream Analytics uses a temporal windowing mechanism (e.g., tumbling, hopping, sliding windows) to process streaming data in micro-batches or continuously. It integrates natively with Azure Event Hubs and IoT Hub for ingestion, and can output alerts to Azure Functions or Logic Apps for immediate action. A real-world scenario is monitoring industrial equipment temperature streams: Stream Analytics can detect when a 5-minute rolling average exceeds a threshold and trigger an alert within seconds.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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.
What to study next
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FAQ
Describe an analytics workload on Azure — This question tests Describe an analytics workload on Azure — Read the scenario before looking for a memorised answer..
The correct answer is: Azure Stream Analytics — Azure Stream Analytics is purpose-built for real-time stream processing, allowing you to define SQL-like queries that run continuously against streaming data from sources like IoT Hub. It can detect anomalies and trigger alerts on the fly, making it the correct core processing engine for this IoT 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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