- A
Azure Stream Analytics
Correct. Stream Analytics can query streaming data from Event Hubs using SQL-like syntax with support for time windows (e.g., Tumbling, Hopping, Sliding) to compute averages and trigger alerts.
- B
Azure Functions
Why wrong: Incorrect. Azure Functions can process events on a per-event basis but lacks native support for time-windowed aggregations and sliding window calculations.
- C
Azure SQL Database
Why wrong: Incorrect. SQL Database is a relational database for stored data; it is not designed for real-time stream processing or handling data directly from Event Hubs.
- D
Azure Logic Apps
Why wrong: Incorrect. Logic Apps orchestrates workflows and integrates services but is not a stream processing engine capable of performing calculations over time windows.
Quick Answer
The correct choice is Azure Stream Analytics because it is purpose-built for real-time stream processing and natively supports sliding window aggregation to compute the average temperature per sensor over a 5-minute window, triggering an alert when the threshold of 100°F is exceeded. Azure Stream Analytics directly ingests data from Azure Event Hubs and applies temporal window functions like SlidingWindow, TumblingWindow, and HoppingWindow, making it the ideal service for this time-based aggregation scenario. On the Microsoft Azure Data Fundamentals DP-900 exam, this question tests your understanding of which Azure service handles real-time analytics versus batch processing—a common trap is confusing Stream Analytics with Azure Data Lake or Azure Synapse, which are better suited for historical analysis. Remember the memory tip: "Sliding windows slide, tumbling windows tumble"—if you need continuous, overlapping time windows for live alerts, think SlidingWindow in Stream Analytics.
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 manufacturing company installs temperature sensors in a factory. Sensor data is streamed to Azure Event Hubs. The company needs to detect when the average temperature of any sensor exceeds 100°F over a 5-minute sliding window and then send an alert. Which Azure service should be used for this real-time stream processing?
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, including the ability to define a 5-minute sliding window over sensor data from Event Hubs, compute the average temperature per sensor, and trigger an alert when the threshold of 100°F is exceeded. It natively integrates with Event Hubs as an input and supports temporal window functions like TumblingWindow, HoppingWindow, and SlidingWindow for exactly this kind of time-based aggregation.
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 can query streaming data from Event Hubs using SQL-like syntax with support for time windows (e.g., Tumbling, Hopping, Sliding) to compute averages and trigger alerts.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Azure Functions
Why it's wrong here
Incorrect. Azure Functions can process events on a per-event basis but lacks native support for time-windowed aggregations and sliding window calculations.
- ✗
Azure SQL Database
Why it's wrong here
Incorrect. SQL Database is a relational database for stored data; it is not designed for real-time stream processing or handling data directly from Event Hubs.
- ✗
Azure Logic Apps
Why it's wrong here
Incorrect. Logic Apps orchestrates workflows and integrates services but is not a stream processing engine capable of performing calculations over time windows.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse Azure Functions with Stream Analytics because both can process Event Hubs data, but Functions lacks native windowing and stateful aggregation capabilities, making it unsuitable for sliding window calculations without significant custom code.
Detailed technical explanation
How to think about this question
Under the hood, Azure Stream Analytics uses a SQL-like query language that compiles into a continuous query plan executed across multiple compute nodes, enabling exactly-once processing semantics and sub-second latency for sliding window operations. The SlidingWindow function in Stream Analytics outputs results every time an event enters or leaves the window, which is critical for detecting threshold breaches in real-time without waiting for a fixed interval. In a real-world factory scenario, this ensures that if a sensor spikes to 105°F for just a few seconds, the alert is triggered immediately rather than at the end of a batch window.
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
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this DP-900 question test?
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, including the ability to define a 5-minute sliding window over sensor data from Event Hubs, compute the average temperature per sensor, and trigger an alert when the threshold of 100°F is exceeded. It natively integrates with Event Hubs as an input and supports temporal window functions like TumblingWindow, HoppingWindow, and SlidingWindow for exactly this kind of time-based aggregation.
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.
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Last reviewed: Jun 11, 2026
This DP-900 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 DP-900 exam.
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