The answer is Azure Stream Analytics. This service is the correct choice because it performs real-time aggregation with Azure Stream Analytics by processing streaming data from Event Hubs using time-based windows, such as a one-minute tumbling window, and then outputting the aggregated results directly to Azure SQL Database for dashboard visualization. On the Microsoft Azure Data Fundamentals DP-900 exam, this scenario tests your understanding of which service handles real-time stream processing versus batch processing; a common trap is confusing Stream Analytics with Azure Data Factory, which is designed for scheduled, batch-oriented data movement rather than continuous, near real-time aggregation. To remember this, think of Stream Analytics as the "live filter" that continuously runs SQL-like queries on data in motion, while Data Factory is the "scheduled mover" for data at rest. A helpful mnemonic is "Stream for Streams, Factory for Files."
DP-900 Describe an analytics workload on Azure Practice Question
This DP-900 practice question tests your understanding of describe an analytics workload on azure. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A data engineering team needs to build a real-time dashboard showing sales totals by region. Sales transactions are streamed from point-of-sale systems into Azure Event Hubs. The team wants to aggregate the data in near real-time (e.g., every minute) and store the results in Azure SQL Database for visualization in Power BI. Which Azure service should they use for the aggregation step?
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, allowing you to define a query that aggregates sales data from Event Hubs over a one-minute tumbling window and output the results directly to Azure SQL Database. This meets the requirement for near real-time aggregation without needing to write custom code or manage infrastructure.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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
Correct. Stream Analytics processes streaming data with SQL-like queries and can aggregate and output to SQL Database in real time.
Related concept
Read the scenario before looking for a memorised answer.
✗
Azure Data Factory
Why it's wrong here
Incorrect. Data Factory is a batch ETL/ELT service and cannot process real-time streaming data.
✗
Azure Synapse Pipelines
Why it's wrong here
Incorrect. Synapse Pipelines are for data movement in batch scenarios, not for real-time stream processing.
✗
Azure Logic Apps
Why it's wrong here
Incorrect. Logic Apps is for orchestrating workflows and integrating services, not for real-time data aggregation.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse Azure Data Factory or Synapse Pipelines (which are batch-oriented) with real-time processing, overlooking that Stream Analytics is the only service among the options purpose-built for continuous, low-latency stream aggregation.
Trap categories for this question
Scenario analysis trap
Incorrect. Synapse Pipelines are for data movement in batch scenarios, not for real-time stream processing.
Detailed technical explanation
How to think about this question
Under the hood, Azure Stream Analytics uses a SQL-like query language with temporal constructs such as TumblingWindow, HoppingWindow, and SlidingWindow to perform time-based aggregations. It leverages a scalable, distributed engine that can handle millions of events per second with sub-second latency, and it supports exactly-once delivery semantics when writing to Azure SQL Database, ensuring no data loss or duplication in the dashboard.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this DP-900 question in full detail.
Describe an analytics workload on Azure — This question tests Describe an analytics workload on Azure — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Azure Stream Analytics — Azure Stream Analytics is the correct choice because it is designed for real-time stream processing, allowing you to define a query that aggregates sales data from Event Hubs over a one-minute tumbling window and output the results directly to Azure SQL Database. This meets the requirement for near real-time aggregation without needing to write custom code or manage infrastructure.
What should I do if I get this DP-900 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A company wants to build a real-time dashboard that visualizes sales data as transactions occur. Which combination of Azure services should they use?
easy
A.Azure Synapse Analytics and PolyBase
B.Azure Data Explorer and Azure Data Lake Storage
✓ C.Azure Stream Analytics and Power BI
D.Azure Analysis Services and Excel
Why C: Azure Stream Analytics is a real-time event processing engine that can ingest streaming data (e.g., from Azure Event Hubs or IoT Hub) and output results directly to Power BI via the built-in Power BI output sink. This combination enables a live dashboard that updates automatically as sales transactions occur, without requiring batch processing or manual refresh.
Variation 2. An organization wants to build a real-time dashboard that visualizes IoT sensor data as it arrives. Which Azure service should they use for processing the streaming data?
easy
A.Azure Analysis Services
B.Azure Data Factory
C.Azure Databricks
✓ D.Azure Stream Analytics
Why D: Azure Stream Analytics is a real-time analytics service designed to process streaming data from sources like IoT devices. It can ingest data from Azure Event Hubs or IoT Hub, apply SQL-based queries to detect patterns or anomalies, and output results to a dashboard or storage with sub-second latency, making it ideal for real-time IoT dashboards.
Last reviewed: Jun 11, 2026
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