Azure Stream Analytics is the correct choice because it is a fully managed, real-time stream processing engine built to handle high-velocity IoT sensor data, perform time-windowed aggregations like computing the average temperature per device every minute, and output results directly to Power BI for near real-time dashboards. This service natively supports a SQL-like query language for defining sliding or tumbling window calculations and offers built-in connectors for Event Hubs and IoT Hub as inputs, making it the most efficient and purpose-built service for this streaming analytics workload. On the DP-900 exam, this scenario tests your understanding of which Azure service handles real-time stream processing versus batch processing with services like Azure Synapse Analytics or Azure Data Lake; a common trap is confusing Stream Analytics with Azure Functions for event processing, but Stream Analytics is specifically designed for continuous, windowed aggregations at scale. Remember the memory tip: “Stream Analytics streams and stats—it’s the SQL-powered window for IoT data that hits Power BI in real time.”
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 collects real-time temperature data from thousands of IoT sensors. They need to build an analytics solution that processes the streaming data, computes the average temperature per device every minute, and outputs the results to a Power BI dashboard for near real-time visualization. Which Azure service should they use for the real-time stream processing?
Correct. Azure Stream Analytics provides a serverless, real-time stream processing engine that can compute aggregations using SQL-like queries and output to Power BI.
B
Azure Data Factory
Why wrong: Incorrect. Azure Data Factory is an orchestration service for moving and transforming data in batches, not for real-time stream processing.
C
Azure Databricks
Why wrong: Incorrect. Azure Databricks can process streaming data using Structured Streaming, but it requires a cluster and is overkill for a simple aggregation pipeline; Azure Stream Analytics is simpler and more cost-effective.
D
Azure SQL Database
Why wrong: Incorrect. Azure SQL Database is a relational database management system, not a stream processing service.
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 a fully managed, real-time stream processing engine designed specifically for scenarios like this: ingesting high-velocity data from IoT sensors, performing time-windowed aggregations (e.g., average temperature per device every minute), and outputting results directly to Power BI for near real-time dashboards. It natively supports SQL-like query language for defining windowed computations and has built-in connectors for both IoT Hub/Event Hubs (input) and Power BI (output), making it the most efficient and purpose-built service for this streaming analytics workload.
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. Azure Stream Analytics provides a serverless, real-time stream processing engine that can compute aggregations using SQL-like queries and output to Power BI.
Related concept
Read the scenario before looking for a memorised answer.
✗
Azure Data Factory
Why it's wrong here
Incorrect. Azure Data Factory is an orchestration service for moving and transforming data in batches, not for real-time stream processing.
✗
Azure Databricks
Why it's wrong here
Incorrect. Azure Databricks can process streaming data using Structured Streaming, but it requires a cluster and is overkill for a simple aggregation pipeline; Azure Stream Analytics is simpler and more cost-effective.
✗
Azure SQL Database
Why it's wrong here
Incorrect. Azure SQL Database is a relational database management system, not a stream processing service.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse Azure Data Factory (a batch ETL tool) with a real-time processing service, or assume that Azure Databricks is always the best choice for streaming because of its Spark foundation, overlooking the simpler, fully managed, and cost-effective alternative of Azure Stream Analytics for straightforward windowed aggregations.
Detailed technical explanation
How to think about this question
Under the hood, Azure Stream Analytics uses a temporal query engine that implements event-time processing with configurable watermarks and late-arrival policies, ensuring accurate windowed aggregations even with out-of-order IoT data. It supports tumbling, hopping, and sliding windows; for a 'per minute' average, a tumbling window of 60 seconds is used, and the service automatically manages checkpointing and exactly-once semantics to prevent data loss or duplication. In a real-world scenario, if sensors occasionally send duplicate or delayed readings, Stream Analytics can handle these via its built-in deduplication and temporal join capabilities without requiring custom code.
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.
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 a fully managed, real-time stream processing engine designed specifically for scenarios like this: ingesting high-velocity data from IoT sensors, performing time-windowed aggregations (e.g., average temperature per device every minute), and outputting results directly to Power BI for near real-time dashboards. It natively supports SQL-like query language for defining windowed computations and has built-in connectors for both IoT Hub/Event Hubs (input) and Power BI (output), making it the most efficient and purpose-built service for this streaming analytics workload.
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
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
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 transportation company collects real-time GPS data from thousands of delivery vehicles. They need to process this streaming data to detect delays and generate alerts when a vehicle is behind schedule. Which Azure service should they use for the stream processing?
easy
A.A. Azure Data Factory
✓ B.B. Azure Stream Analytics
C.C. Azure Data Lake Analytics
D.D. Azure Analysis Services
Why B: Azure Stream Analytics is the correct choice because it is a fully managed, real-time stream processing engine designed to handle high-velocity data from sources like IoT devices and GPS sensors. It can ingest streaming data from Azure Event Hubs or IoT Hub, apply SQL-based queries to detect patterns such as delays, and output alerts to sinks like Azure Functions or Power BI in near real-time.
Variation 2. A retail company receives a continuous stream of customer orders from their website via Azure Event Hubs. They also receive daily inventory updates from suppliers as CSV files uploaded to Azure Blob Storage. The company needs to calculate real-time order fulfillment availability by joining the streaming orders with the latest inventory snapshot. Additionally, they generate nightly sales reports from historical order data. Which Azure service should they use for the real-time processing component?
easy
A.Azure Data Factory
✓ B.Azure Stream Analytics
C.Azure Databricks
D.Azure Synapse Pipelines
Why B: Azure Stream Analytics is the correct choice because it is designed for real-time data processing, allowing you to join streaming data from Event Hubs with static or reference data (like the latest inventory snapshot from Blob Storage) using SQL-like queries. This enables the calculation of real-time order fulfillment availability as orders arrive, which is the core requirement.
Last reviewed: Jun 11, 2026
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