DP-900 Describe an analytics workload on Azure Practice Question
An organization wants to build a real-time dashboard that visualizes IoT sensor data as it arrives. Which Azure service should they use for processing the streaming data?
⚠ Common exam trap
Microsoft often tests the distinction between batch processing (Data Factory) and real-time stream processing (Stream Analytics), and candidates mistakenly choose Azure Databricks because they associate it with 'big data' without recognizing Stream Analytics as the simpler, purpose-built service for streaming IoT dashboards.
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 a real-time analytics service designed to process streaming data from sources like IoT devices. It can ingest data from Azure Event Hubs or IoT Hub, apply SQL-based queries to detect patterns or anomalies, and output results to a dashboard or storage with sub-second latency, making it ideal for real-time IoT dashboards.
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 Analysis Services
Why it's wrong here
Azure Analysis Services is a platform for building enterprise-scale semantic models that aggregate historical data via tabular or multidimensional projects. It relies on in-memory caches refreshed on a schedule or manually, making it inherently batch-oriented, so it cannot ingest live event streams or deliver the sub-second latency a real-time dashboard requires. A dashboard served by Analysis Services would refresh only as often as the model, not continuously.
- ✗
Azure Data Factory
Why it's wrong here
Azure Data Factory is a data integration and orchestration service that moves and transforms data in batches using pipelines, with activities executed on schedules or triggered by events from files or blobs. While you can set a pipeline to run very frequently, it is not designed to process individual streaming events at the moment they occur; it copies or transforms stored data, which adds inherent latency. A dashboard built on Data Factory would show data only after a pipeline run completes, so it does not meet true real-time requirements.
- ✗
Azure Databricks
Why it's wrong here
Azure Databricks, using Apache Spark's Structured Streaming, can process streaming data at scale, but it is a general-purpose analytics and machine learning platform that requires cluster management, code development, and careful configuration for even simple workloads. In a real-time dashboard scenario, it adds operational complexity and higher latency between event ingestion and visualization, because its native Power BI connectivity typically queries a Delta table after micro-batches land rather than pushing results low-latency. Stream Analytics, by contrast, is purpose-built for this and pushes directly to Power BI streaming datasets.
- ✓
Azure Stream Analytics
Why this is correct
Azure Stream Analytics is a fully managed stream processing engine that executes SQL-like queries continuously on data arriving from sources like Azure Event Hubs, IoT Hub, or Blob Storage, and it can output directly to Power BI for real-time dashboards. It supports time-based windowing, aggregations, and filtering, allowing you to compute meaningful metrics like averages or counts over a defined time slice with latency in the range of a second. Being purpose-built for real-time stream processing, it offers a simple declarative query model and the easiest integration path to live visuals, making it the correct choice here.
Go deeper
Related to this question
Learn chapter
Data Roles and Core Concepts
Key term
IoT Hub
IoT Hub is a managed cloud service in Azure that acts as a central message hub for secure, bidirectional communication between IoT devices and the cloud.
Key term
Dashboard
A dashboard is a visual display of key metrics and data points that helps IT professionals monitor, analyze, and manage systems or processes in real time.
About these practice questions
This DP-900 question is part of Courseiva's 820-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This DP-900 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DP-900 exam.