DP-900 Describe an analytics workload on Azure Practice Question
Which TWO Azure services can be used to build a real-time analytics solution that ingests streaming data and provides dashboards with low latency? (Choose two.)
⚠ Common exam trap
Test-takers frequently confuse batch-oriented services like Azure Data Factory or storage-only services like Data Lake Storage Gen2 with real-time analytics capabilities, or they assume HDInsight with Spark alone provides built-in dashboards, when in fact it requires a separate visualization layer.
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
✓
Power BI
Azure Stream Analytics (E) is correct because it is a fully managed, real-time stream processing engine that ingests data from sources such as Azure Event Hubs, IoT Hub, or Blob Storage, runs continuous SQL-like queries over the streaming data, and outputs results to sinks like Power BI with sub-second to low-latency processing. Power BI (A) is correct because it provides the dashboarding and visualization layer for real-time analytics, supporting streaming datasets and push datasets that render live tiles and dashboards with low latency when fed by services such as Azure Stream Analytics. Together, Stream Analytics handles ingestion and real-time computation while Power BI delivers the low-latency dashboards, which is exactly the scenario described. Azure Data Factory (B) is a batch-oriented data integration and orchestration service, not a real-time streaming engine, so it does not fit. Azure Data Lake Storage Gen2 (C) is a scalable storage layer for batch and analytical workloads, not a streaming ingestion or dashboarding service. Azure HDInsight with Spark (D) can process streaming data via Spark Structured Streaming, but it is a cluster-based big data service rather than a purpose-built low-latency real-time analytics and dashboarding solution, and it does not itself provide 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.
- ✓
Power BI
Why this is correct
Power BI supports real-time analytics by connecting to streaming sources through DirectQuery or ingesting data into streaming datasets, which update visuals automatically as new events arrive. This enables live dashboards that refresh within seconds, making Power BI the presentation layer for real-time scoring and monitoring scenarios.
- ✗
Azure Data Factory
Why it's wrong here
Azure Data Factory is a data integration and orchestration service that moves and transforms data on a schedule, typically in batch mode using pipelines and control flow. It does not offer low-latency event ingestion or sub-second stream processing, so it cannot serve as the engine for a real-time dashboard.
- ✗
Azure Data Lake Storage Gen2
Why it's wrong here
Azure Data Lake Storage Gen2 provides hierarchical, petabyte-scale object storage for analytical data, but it is purely a storage service with no compute capabilities to process or query live streams. Real-time architectures may land streaming data into it, but the lake itself does not generate or refresh dashboard visuals without an additional processing service.
- ✗
Azure HDInsight with Spark
Why it's wrong here
Azure HDInsight with Spark can run Spark Structured Streaming, but its micro-batch model introduces seconds of latency and requires cluster management, making it less suitable for true real-time dashboards. While it can process streaming data, it is optimized for batch analytics and lacks the built-in, latency-sensitive dashboard integration found in a dedicated stream processing service.
- ✓
Azure Stream Analytics
Why this is correct
Azure Stream Analytics is a managed stream-processing engine that runs continuous SQL-like queries over data from IoT hubs, event hubs, or blob storage, emitting results within milliseconds. It directly integrates with Power BI to push live results to dashboards, making it the core real-time processing component for scenarios like telemetry monitoring or fraud detection.
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Key term
Azure Data Factory
Azure Data Factory is a cloud-based data integration service that lets you create, schedule, and orchestrate data pipelines to move and transform data from various sources to destinations.
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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