DP-900 Describe an analytics workload on Azure Practice Question
A financial services company processes real-time stock trade data from multiple exchanges. Trades are ingested into Azure Event Hubs. The company needs to compute a 5-minute sliding window average of trade prices per stock symbol and ensure that each trade is processed exactly once within the window. The aggregated results must be stored in Azure SQL Database for historical reporting and also sent to a Power BI dashboard for near real-time visualization. Which Azure service should be used for the real-time processing?
⚠ Common exam trap
It's easy for candidates to confuse Azure Event Hubs (a data ingestion service) with a processing engine, or assume that Azure Databricks is the only option for streaming analytics, overlooking the simpler, fully managed, and cost-effective Azure Stream Analytics for straightforward windowed aggregations.
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 purpose-built for real-time stream processing with native support for time-based windowing (e.g., 5-minute sliding window) and exactly-once semantics when used with Azure Event Hubs as input and Azure SQL Database as output. It can directly compute the sliding window average of trade prices per stock symbol and output results to both Azure SQL Database for historical storage and Power BI for near real-time visualization, all without requiring additional code or infrastructure management.
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 Stream Analytics
Why this is correct
Azure Stream Analytics is a fully managed, serverless stream-processing engine purpose-built for real-time calculations such as the average stock price over a sliding window. Its temporal query language natively supports tumbling, hopping, and sliding windows, and it guarantees exactly-once processing semantics while writing to multiple sinks like Azure SQL Database and Power BI in the same job. This makes it the ideal fit for a low-latency, sub-minute aggregation scenario without infrastructure management.
- ✗
Azure Databricks with Structured Streaming
Why it's wrong here
Azure Databricks with Structured Streaming, whilst capable of real-time processing, windowing, and exactly-once semantics, introduces operational overhead due to cluster management and scaling. This contrasts with the scenario's implied need for a fully managed, serverless streaming analytics service. It is tempting because it excels in complex, custom data transformations, machine learning integration, and when a unified analytics platform is required, offering extensive control over the compute environment for bespoke solutions.
- ✗
Azure Data Factory
Why it's wrong here
Azure Data Factory is a cloud-based ETL and orchestration service that primarily moves and transforms data in batches or on scheduled, recurring pipelines. It has no built-in temporal window operators for live streams, and any attempt to compute per-minute averages would require polling from Event Hubs and then calling custom compute — introducing latency and complexity. Its strength is dependency-driven, at-scale data movement and transformation for analytical stores, not continuous sub-second event processing.
- ✗
Azure Event Hubs
Why it's wrong here
Azure Event Hubs is a massively scalable ingestion layer that simply captures, partitions, and retains incoming events for streaming clients. It does not host user-defined query logic, so it cannot calculate a sliding-window average itself; to obtain that metric, a downstream processor such as Stream Analytics or Databricks must consume the events from a consumer group. Without that separate computation engine, the raw market ticks remain only raw data.
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
Stream processing
Stream processing is a data processing method that continuously analyzes and acts on data in real time as it arrives, rather than storing it first and processing it later.
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