Quick Answer
The answer is Azure Stream Analytics. This service is the correct choice because it is purpose-built for real-time stream processing, allowing you to aggregate events from Azure Event Hubs over a one-minute tumbling window and update a real-time dashboard every minute. Stream Analytics natively supports windowing functions like TumblingWindow and HoppingWindow, enabling you to define precise aggregation intervals directly in your query, and it handles high-throughput, low-latency data streams from Event Hubs with minimal configuration. On the AZ-204 exam, this scenario tests your understanding of Azure’s stream processing services versus batch or messaging-only solutions; a common trap is choosing Azure Functions or Logic Apps, which lack native windowing and are better for event-driven triggers rather than continuous aggregation. Remember the memory tip: “Stream Analytics is the window to your Event Hubs data”—if you need time-based windows and dashboard updates, Stream Analytics is the only first-party service that does both natively.
This AZ-204 practice question tests your understanding of develop azure compute solutions. 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.
You are building a real-time dashboard that displays data from Azure Event Hubs. You need to aggregate events over a one-minute window and update the dashboard every minute. Which Azure service should you use?
Azure Stream Analytics
Stream Analytics is designed for real-time analytics on streaming data.
Azure Data Factory
Why wrong: Data Factory is for ETL, not real-time streaming.
Azure Analysis Services
Why wrong: Analysis Services is for analytical models, not real-time stream processing.
Azure Logic Apps
Why wrong: Logic Apps are for workflows, not real-time stream processing.
Answer choices
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 aggregate events from Azure Event Hubs over a one-minute tumbling window and output results to a dashboard or sink. It natively supports windowing functions (e.g., TumblingWindow, HoppingWindow) and can handle high-throughput, low-latency data streams, making it ideal for updating a dashboard every minute.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
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
Stream Analytics is designed for real-time analytics on streaming data.
Related concept
Read the scenario before looking for a memorised answer.
Azure Data Factory
Why it's wrong here
Data Factory is for ETL, not real-time streaming.
Azure Analysis Services
Why it's wrong here
Analysis Services is for analytical models, not real-time stream processing.
Azure Logic Apps
Why it's wrong here
Logic Apps are for workflows, not real-time stream processing.
Common exam traps
The trap here is that candidates often confuse Azure Stream Analytics with Azure Data Factory, mistakenly thinking Data Factory can handle real-time streaming when it is actually designed for batch-oriented data movement and transformation.
Detailed technical explanation
Under the hood, Azure Stream Analytics uses a SQL-like query language with temporal operators such as TumblingWindow(second, 60) to group events into fixed-size, non-overlapping windows. It leverages a distributed, scalable engine that partitions the stream across multiple compute nodes, ensuring exactly-once processing semantics for windowed aggregates. In a real-world scenario, this allows a dashboard to display metrics like average CPU usage or error counts per minute without missing or duplicating events, even under high ingestion rates.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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.
What to study next
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FAQ
Develop Azure compute solutions — This question tests Develop Azure compute solutions — Read the scenario before looking for a memorised answer..
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 aggregate events from Azure Event Hubs over a one-minute tumbling window and output results to a dashboard or sink. It natively supports windowing functions (e.g., TumblingWindow, HoppingWindow) and can handle high-throughput, low-latency data streams, making it ideal for updating a dashboard every minute.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
This AZ-204 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AZ-204 exam.
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