DP-900 Describe an analytics workload on Azure Practice Question
A company runs a real-time dashboard in Power BI that displays sales data from Azure Synapse Analytics. The dashboard must show data with less than 5 seconds of latency. Which Azure service should be used to ingest streaming sales events into Azure Synapse Analytics?
⚠ Common exam trap
A common mix-up: candidates confuse Azure Data Factory (a batch ETL tool) with a real-time streaming service, or they assume Azure SQL Database can handle streaming ingestion, but neither supports the required sub-5-second latency for continuous data flow into Synapse Analytics.
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 designed for real-time stream processing, capable of ingesting high-velocity streaming sales events and outputting them to Azure Synapse Analytics with sub-second latency. This meets the requirement of less than 5 seconds of latency for the Power BI dashboard.
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 SQL Database
Why it's wrong here
Azure SQL Database is a managed relational database service optimized for transactional OLTP workloads, storing current and historical data in rowstore tables. It cannot natively subscribe to a streaming event source; ingesting real-time telemetry would require you to write batches or use an external ingestion layer, which naturally adds latency. While it can host dashboard query results, using it as the direct data source for a sub-5-second streaming dashboard would require frequent inserts that create lock contention and miss the continuous, low-latency processing requirement.
- ✓
Azure Stream Analytics
Why this is correct
Azure Stream Analytics is a purpose-built serverless stream processing engine that continuously executes SQL-like queries over data from Event Hubs, IoT Hub, or Blob storage with sub-second latency. Its temporal windows and event-time handling allow real-time aggregations, and it has a native Power BI output connector that pushes data directly to the dashboard. This matches the requirement to display a live dashboard with less than five seconds of freshness from ingestion to visualization.
- ✗
Azure Data Factory
Why it's wrong here
Azure Data Factory is an orchestration and ETL service that moves and transforms data in batches based on schedules, tumbling windows, or event-triggered pipeline runs. A pipeline executes discrete activities with start/stop boundaries, so it cannot continuously process every event as it arrives. Even if you trigger it frequently, the overhead of job scheduling, monitoring, and dataset refreshes pushes end-to-end latency well beyond the sub-5-second freshness expected from a real-time dashboard.
- ✗
Azure Databricks
Why it's wrong here
Azure Databricks is an Apache Spark-based analytics platform that can run Structured Streaming, but its default operation is micro-batch processing, where data is collected into small batches and computed on cluster resources. The need to allocate clusters, plan query stages, and checkpoint state introduces seconds to minutes of latency and requires cluster management overhead. For a simple, low-latency ingestion-to-dashboard pipeline, this is overly complex compared to a dedicated stream processor.
Go deeper
Related to this question
Learn chapter
Data Roles and Core Concepts
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.
Key term
Service
A service is a software component or system that performs a specific function and is available to be used by other programs or users over a network.
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