DP-900 Describe an analytics workload on Azure Practice Question
A retail company needs to analyze sales transactions as they occur to detect fraud patterns and immediately block suspicious orders. They also need to run daily batch reports on historical sales data. Which combination of Azure services should they use to meet both real-time and batch processing requirements?
⚠ Common exam trap
Watch out — candidates often confuse Azure Data Factory's orchestration capabilities with real-time processing, or assume that a single service like Stream Analytics can handle both streaming and batch analytics, when in fact each service is specialized for a distinct workload type.
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 for real-time processing and Azure Synapse Analytics for batch analytics
Azure Stream Analytics is purpose-built for real-time data streaming and can process sales transactions as they occur to detect fraud patterns and block suspicious orders immediately. Azure Synapse Analytics provides a unified analytics platform that can run large-scale batch queries on historical sales data for daily reports, making this combination ideal for both real-time and batch processing needs.
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 for real-time processing and Azure Synapse Analytics for batch analytics
Why this is correct
Azure Stream Analytics executes continuous SQL-like queries over data as it arrives in Event Hubs or IoT Hub, providing low-latency aggregations while transactions occur. Azure Synapse Analytics is a massively parallel processing data warehouse optimized for large-scale batch T-SQL queries over historical data, making it the right home for daily or periodic reports. Together they deliver both real-time insights and deep historical analysis.
- ✗
Azure Data Factory for both real-time and batch processing
Why it's wrong here
Azure Data Factory is an orchestration service for scheduled, trigger-based ETL/ELT pipelines that copy and transform data at rest; it has no continuous query engine or event-processing runtime, so it cannot analyze a high-velocity stream of sales transactions with sub-second latency. Attempting to use Data Factory for real-time processing would require unnatural polling patterns or very short re-triggers, which is neither cost-effective nor a true streaming solution. It can manage batch ingestion and transformation, but it cannot serve both real-time and batch processing in the way the requirement needs.
- ✗
Azure Logic Apps for real-time processing and Azure Synapse Analytics for batch analytics
Why it's wrong here
Azure Logic Apps is a low-code workflow engine designed for connecting APIs and automating business processes through triggers and actions, but it processes events one at a time and lacks the windowed aggregation, partitioning, and checkpointing required for high-throughput streaming analytics. While pairing Logic Apps with Synapse Analytics for batch reporting is architecturally coherent, the real-time processing leg would fail under a heavy stream of sales transactions, so this option does not satisfy the stated need. It is a workflow integration tool, not a stream processor.
- ✗
Azure Stream Analytics for both real-time and batch processing
Why it's wrong here
Azure Stream Analytics is built for unbounded continuous streams and supports tumbling, hopping, sliding, and session window aggregations, but it is not a data warehouse; it does not provide the massive parallel processing optimizer or columnstore storage needed to efficiently run large, ad hoc batch queries over months of historical sales data. Replaying all historical data through Stream Analytics each day to generate a report would be slow, expensive, and outside its intended programming model. Historical reporting belongs in a dedicated warehouse such as Synapse, where data is persisted and indexed for set-based batch queries.
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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
Batch processing
Batch processing is a method of running high-volume, repetitive data jobs where a group of transactions is collected, processed together automatically, and then results are produced without real-time user interaction.
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