AZ-204 Practice Question: Connect to and consume Azure services and third-party services
You need to process large volumes of streaming data from IoT devices in near real-time. The processed data must be stored in Azure Cosmos DB for further analysis. Which Azure service should you use for stream processing?
⚠ Common exam trap
Many exam-takers confuse Azure Databricks as the only option for streaming analytics due to its Spark Structured Streaming capability, overlooking that Azure Stream Analytics is the simpler, fully managed service specifically designed for near real-time processing without the need for cluster management.
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
Azure Stream Analytics is purpose-built for real-time stream processing, capable of ingesting large volumes of data from sources like Azure Event Hubs or IoT Hub, applying SQL-based queries, and outputting results directly to Azure Cosmos DB. This aligns perfectly with the requirement for near real-time processing and storage in Cosmos DB.
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 Batch
Why it's wrong here
Azure Batch is a service designed for running large-scale parallel and high-performance computing (HPC) applications efficiently in the cloud. It excels at executing discrete, finite batch jobs, such as rendering or financial risk modeling, by managing a pool of virtual machines. However, it is fundamentally unsuited for continuous, real-time ingestion and processing of streaming data from IoT devices, which requires a persistent, low-latency processing pipeline rather than scheduled job execution.
- ✗
Azure Databricks
Why it's wrong here
Azure Databricks is an Apache Spark-based analytics platform optimized for big data and AI workloads, capable of handling stream processing through structured streaming. While powerful for complex real-time analytics, machine learning, and large-scale data engineering, it can be an overly complex and expensive solution for straightforward stream ingestion, transformation, and direct output to a NoSQL database like Cosmos DB. A dedicated, fully managed stream processing service is often more efficient for simpler IoT data pipelines.
- ✗
Azure Data Lake Storage
Why it's wrong here
Azure Data Lake Storage (ADLS) is a highly scalable and secure storage solution built for big data analytics workloads, designed to store massive amounts of data in its native format. Its primary function is data persistence and management, not real-time data processing or transformation. While streaming data can be ingested and stored in ADLS, the service itself does not provide the compute capabilities required to continuously analyze, filter, or aggregate that data as it arrives from IoT sources.
- ✓
Azure Stream Analytics
Why this is correct
Azure Stream Analytics is a fully managed, real-time analytics service designed specifically for processing large volumes of streaming data with low latency. It enables users to perform complex event processing, aggregations, and transformations on data from sources like IoT Hub using a SQL-like query language. Its native integration with Azure IoT Hub for input and Azure Cosmos DB for output makes it the ideal, purpose-built solution for real-time IoT data pipelines.
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Senior Network & Security Engineer · founder of Courseiva
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