DP-900 Describe an analytics workload on Azure Practice Question
A manufacturing company collects real-time temperature data from thousands of IoT sensors. They need to build an analytics solution that processes the streaming data, computes the average temperature per device every minute, and outputs the results to a Power BI dashboard for near real-time visualization. Which Azure service should they use for the real-time stream processing?
⚠ Common exam trap
Candidates often confuse Azure Data Factory (a batch ETL tool) with a real-time processing service, or assume that Azure Databricks is always the best choice for streaming because of its Spark foundation, overlooking the simpler, fully managed, and cost-effective alternative of Azure Stream Analytics for straightforward windowed 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
✓
Azure Stream Analytics
Azure Stream Analytics is the correct choice because it is a fully managed, real-time stream processing engine designed specifically for scenarios like this: ingesting high-velocity data from IoT sensors, performing time-windowed aggregations (e.g., average temperature per device every minute), and outputting results directly to Power BI for near real-time dashboards. It natively supports SQL-like query language for defining windowed computations and has built-in connectors for both IoT Hub/Event Hubs (input) and Power BI (output), making it the most efficient and purpose-built service for this streaming analytics workload.
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 Stream Analytics
Why this is correct
Azure Stream Analytics is a fully managed, serverless real-time stream processing engine that ingests telemetry from IoT Hub or Event Hubs, applies SQL-like temporal queries to compute rolling aggregations such as average temperature, and writes directly to Power BI via its built-in output sink. Its tumbling, hopping, and sliding window functions are purpose-built for sub-minute analytics on high-velocity sensor data, which is why this is the correct choice.
- ✗
Azure Data Factory
Why it's wrong here
Azure Data Factory is an extract-transform-load (ETL) orchestration service designed for scheduled, batch-oriented data movement and pipelined transformations, not for continuous processing of live telemetry. It lacks native temporal windowing functions and cannot maintain a low-latency connection to Power BI for dashboard refreshes every few seconds; instead, it would be used to periodically copy raw temperature files into storage before separate processing.
- ✗
Azure Databricks
Why it's wrong here
Azure Databricks is a unified analytics platform designed for batch and stream processing using Apache Spark, but it lacks native integration with Power BI for sub-minute dashboard refreshes; the correct service, Azure Stream Analytics, directly outputs to Power BI via its built-in sink without requiring additional orchestration. It is tempting because Databricks excels at complex transformations and machine learning on streaming data, making it the right choice if the scenario demanded advanced analytics like anomaly detection rather than simple aggregation and direct dashboard output.
- ✗
Azure SQL Database
Why it's wrong here
Azure SQL Database is a managed relational database system that stores and queries persistable tabular data using standard T-SQL; it is not a stream processing platform and cannot ingest unbounded real-time events directly from sensors. Even though Power BI can query SQL Database, the absence of built-in windowing and event-time handling means you would need another service to aggregate the temperature stream before it can be visualized sub-minute.
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Data Roles and Core Concepts
Key term
Azure Stream Analytics
Azure Stream Analytics is a fully managed, real-time data processing service that analyzes and transforms high volumes of streaming data from various sources to deliver low-latency insights and trigger actions.
Key term
Stream processing
Stream processing is a data processing method that continuously analyzes and acts on data in real time as it arrives, rather than storing it first and processing it later.
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