DP-900 Describe an analytics workload on Azure Practice Question
A company wants to build a real-time analytics dashboard for IoT sensor data. Which combination of Azure services should they use?
⚠ Common exam trap
Watch out — candidates often confuse batch processing services (like Azure Data Factory or HDInsight) with real-time streaming services, or assume that any database (like Azure SQL) can handle high-velocity streaming data, but only Stream Analytics provides the necessary event-time processing and low-latency output for live 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 and Power BI
Azure Stream Analytics is a real-time event processing engine that can ingest IoT sensor data from sources like Azure Event Hubs, apply SQL-based queries to detect patterns or anomalies, and output results directly to Power BI for live dashboard visualization. This combination provides end-to-end streaming analytics with sub-second latency, which is essential for real-time 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 HDInsight and Azure Databricks
Why it's wrong here
Azure HDInsight and Azure Databricks are both big-data processing platforms that rely on cluster-based compute and are designed for large-scale batch or near-real-time workloads. HDInsight runs frameworks like Spark and Hive for distributed processing, while Databricks uses Structured Streaming, which is micro-batch at heart and can introduce seconds of latency. Neither service is built for continuous, sub-second query serving directly to an interactive dashboard, and they lack native Power BI integration that updates a live tile the moment a stream event arrives. To build a true real-time dashboard, you need a dedicated stream-processing engine like Azure Stream Analytics, not a general-purpose analytics cluster.
- ✗
Azure Logic Apps and Azure SQL Database
Why it's wrong here
Azure Logic Apps is an integration and workflow orchestration service, meant to connect APIs, schedule jobs, and automate business processes—not to handle high-throughput, unbounded event streams or perform analytics on them. Azure SQL Database is a relational data store that supports queries against persisted data, but it does not process streaming data in memory or provide continuous windowed aggregations over live events. While Logic Apps could be triggered by events and insert rows into SQL Database, that path would store data first and then query it, creating a batch-like latency that is unacceptable for sub-second real-time dashboards. This combination is for operational automation and transactional storage, not for real-time analytics.
- ✗
Azure Data Factory and Azure Analysis Services
Why it's wrong here
Azure Data Factory is an ETL/data-integration service that moves and transforms data in batches, typically on a scheduled or tumbling-window basis—it is not a stream-processing engine. Azure Analysis Services (AAS) provides tabular models and in-memory OLAP for offline, semantic-layer analytics, but those models are refreshed from a data source, not updated continuously from a live event stream. Neither component can ingest a high-velocity stream directly and push a live dashboard update; Data Factory would have to land data in storage first, and AAS would only serve data after a model refresh. This pair supports historical, interactive BI, but it cannot meet real-time latency requirements.
- ✓
Azure Stream Analytics and Power BI
Why this is correct
Azure Stream Analytics is a fully managed stream-processing service that runs SQL-like queries against unbounded data from sources like Event Hubs or IoT Hub, supporting temporal windows such as tumbling, hopping, and sliding to compute live aggregates. It has a native output sink to Power BI, which pushes results into a Power BI dataset that dashboard tiles can subscribe to for near-real-time updates—typically within a few seconds. Power BI then renders the streaming data visually, enabling a dashboard to stay current without manual refresh or batch scheduling. This combination is exactly what the company needs for a real-time analytics dashboard because Event Hubs delivers the stream, ASA processes it continuously, and Power BI displays it live.
Go deeper
Related to this question
Learn chapter
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
Data
Data is raw, unprocessed information, like numbers, words, or measurements, that can be stored, processed, and analyzed by computers.
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