DP-900 Describe an analytics workload on Azure Practice Question
A company ingests streaming data from IoT devices into Azure Event Hubs. They need to perform real-time analytics on the data, such as aggregating temperature readings over 5-minute windows and triggering alerts when thresholds are exceeded. They also want to store the processed data in a data warehouse for historical analysis. Which Azure service should they use for the real-time processing?
⚠ Common exam trap
It's easy for candidates to confuse Azure Stream Analytics with Azure Databricks, thinking that any Spark-based service is required for streaming, but Stream Analytics is the simpler, fully managed service specifically designed for real-time analytics on Azure Event Hubs without needing to manage clusters or write complex code.
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, allowing you to define SQL-like queries that aggregate data over tumbling or hopping windows (e.g., 5-minute windows) and trigger alerts based on thresholds. It integrates directly with Azure Event Hubs as a source and can output processed results to Azure Synapse Analytics or other data warehouses for historical storage, making it the correct choice for this real-time 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 Data Factory
Why it's wrong here
Azure Data Factory is a cloud-based ETL/ELT orchestration service designed for batch data movement and transformation. It uses pipelines triggered on schedules or events to copy data between stores, invoke compute like HDInsight or Databricks, and schedule data flows—but it does not provide native stream processing primitives like event-time windowed aggregations or continuous queries. Its paradigm is 'transform in batch and move,' not 'consume and analyze a live stream with sub-second latency,' so it cannot directly meet the real-time aggregation requirement.
When this WOULD be correct
A question asks: 'Which Azure service should be used to orchestrate and schedule data movement from on-premises SQL Server to Azure Blob Storage on a nightly basis?' In that scenario, Azure Data Factory is the correct answer.
- ✓
Azure Stream Analytics
Why this is correct
Azure Stream Analytics is a fully managed real-time analytics service designed specifically for stream processing. It ingests high-throughput data from sources like IoT Hub or Event Hubs, applies SQL-like queries with built-in windowing functions (tumbling, hopping, sliding, session), and can perform aggregations, filtering, and alerting with sub-second latency. Its output sinks include Azure Data Lake, Synapse Analytics, and Power BI, making it the ideal choice for real-time IoT telemetry processing without managing infrastructure.
- ✗
Azure Databricks
Why it's wrong here
Azure Databricks, with Spark Structured Streaming, can indeed process streaming data and perform complex micro-batch or continuous processing, including windowed aggregations. However, for a simple IoT ingestion-to-warehouse pipeline that only requires real-time aggregations, Databricks introduces significant operational overhead: you must provision and autoscale clusters, manage Spark configuration, and handle workloads that are far more powerful than the lightweight, serverless Stream Analytics approach. It is better suited for advanced transformations, machine learning, or complex stateful stream processing, not basic windowed counting or averaging.
When this WOULD be correct
Azure Databricks would be the correct answer if the question required complex machine learning model inference on streaming data, or if the processing needed custom transformations using Python/Scala/R that go beyond what Stream Analytics can handle. For example: 'A company needs to run a pre-trained anomaly detection model on streaming IoT data in real-time, and also perform custom feature engineering using Python libraries.'
- ✗
Azure Logic Apps
Why it's wrong here
Azure Logic Apps is an integration platform for orchestrating workflows, connecting to hundreds of SaaS and on-premises connectors via triggers and actions. While it can receive messages from Event Hubs and perform simple transformations, it is not built for high-throughput, low-latency stream analytics—it lacks native windowed aggregation primitives and its execution model is optimized for business process automation, not continuous analytical queries over a live data stream.
When this WOULD be correct
A question asking for a service to orchestrate a business process that reacts to events from Event Hubs, such as sending an email or creating a ticket when a threshold is exceeded, without needing complex analytics or windowed aggregations.
Option-by-option analysis
Why each answer is right or wrong
Understanding why wrong answers are wrong — and when they would be correct — is what separates a 750 score from a 900. The DP-900 exam frequently reuses these exact scenarios with slightly different constraints.
✓Azure Stream AnalyticsCorrect answer▾
Why this is correct
Azure Stream Analytics is a fully managed real-time analytics service designed specifically for stream processing. It ingests high-throughput data from sources like IoT Hub or Event Hubs, applies SQL-like queries with built-in windowing functions (tumbling, hopping, sliding, session), and can perform aggregations, filtering, and alerting with sub-second latency. Its output sinks include Azure Data Lake, Synapse Analytics, and Power BI, making it the ideal choice for real-time IoT telemetry processing without managing infrastructure.
✗Azure Data FactoryWrong answer — click to see why▾
Why this is wrong here
Azure Data Factory is an ETL and data orchestration service, not designed for real-time stream processing. It cannot perform windowed aggregations or trigger alerts on streaming data from Event Hubs.
★ When this WOULD be the correct answer
A question asks: 'Which Azure service should be used to orchestrate and schedule data movement from on-premises SQL Server to Azure Blob Storage on a nightly basis?' In that scenario, Azure Data Factory is the correct answer.
Why candidates choose this
Candidates may confuse Data Factory's data movement capabilities with real-time processing, or think it can handle streaming data because it integrates with various sources.
✗Azure DatabricksWrong answer — click to see why▾
Why this is wrong here
Azure Databricks is a big data analytics platform that can process streaming data, but it is overkill for simple real-time aggregations and alerts on IoT data. The question specifically asks for a service to perform real-time analytics like windowed aggregations and threshold alerts, which is exactly what Azure Stream Analytics is designed for with its SQL-like language and built-in windowing functions.
★ When this WOULD be the correct answer
Azure Databricks would be the correct answer if the question required complex machine learning model inference on streaming data, or if the processing needed custom transformations using Python/Scala/R that go beyond what Stream Analytics can handle. For example: 'A company needs to run a pre-trained anomaly detection model on streaming IoT data in real-time, and also perform custom feature engineering using Python libraries.'
Why candidates choose this
Candidates may choose Azure Databricks because they associate it with 'real-time' and 'analytics' due to its Spark Structured Streaming capabilities, and they might overestimate the complexity of the required processing, thinking that a full Spark environment is needed for any streaming task.
✗Azure Logic AppsWrong answer — click to see why▾
Why this is wrong here
Azure Logic Apps is designed for workflow automation and integration, not for real-time stream processing with windowed aggregations and alerts on streaming data from Event Hubs.
★ When this WOULD be the correct answer
A question asking for a service to orchestrate a business process that reacts to events from Event Hubs, such as sending an email or creating a ticket when a threshold is exceeded, without needing complex analytics or windowed aggregations.
Why candidates choose this
Candidates may confuse Logic Apps' ability to trigger on events with the need for real-time analytics, overlooking that Logic Apps lacks native stream processing capabilities like tumbling windows and built-in analytics functions.
Analysis generated from the official DP-900blueprint and verified against question context. The “when correct” sections are what AI assistants cite when candidates ask “what’s the difference between these options?”
Go deeper
Related to this question
Learn chapter
Data Roles and Core Concepts
Key term
Trigger
In Azure data services, a trigger is a predefined automatic action that initiates a process when a specific event occurs, such as data arriving or a schedule being met.
Key term
Azure Synapse Analytics
Azure Synapse Analytics is a cloud-based data integration, warehousing, and analytics service that brings together big data and data warehouse capabilities under one platform.
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Senior Network & Security Engineer · founder of Courseiva
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