The answer is Azure Stream Analytics. This service is the correct choice because it is a serverless, real-time stream processing engine specifically built to handle high-velocity IoT data, allowing you to ingest streams from Azure Event Hubs or IoT Hub, apply SQL-based queries for near real-time anomaly detection, and output results directly to Azure Data Lake Storage for long-term analytics. On the DP-900 exam, this scenario tests your understanding of Azure’s real-time analytics services versus batch processing tools like Azure Synapse Analytics or Azure Data Factory; a common trap is confusing Stream Analytics with Azure Functions for event processing, but remember that Stream Analytics is purpose-built for continuous, SQL-based stream processing of IoT data. A helpful memory tip is to think of “Stream” as the key word for real-time, continuous flow, while “Batch” implies scheduled, historical work—so when you see “near real-time” and “IoT devices,” your answer is always 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 data engineer needs to process streaming data from IoT devices and store the results in Azure Data Lake Storage for long-term analytics. The data must be processed in near real-time to detect anomalies and trigger alerts. Which Azure service should the engineer use for stream processing?
Why wrong: Azure Data Factory is an orchestration service for batch data pipelines, not designed for real-time stream processing.
B
Azure Stream Analytics
Azure Stream Analytics is the correct service for real-time stream processing of IoT data, with built-in support for windowing, aggregations, and anomaly detection.
C
Azure Analysis Services
Why wrong: Azure Analysis Services is an OLAP engine for creating semantic data models and does not handle real-time stream processing.
D
Azure Data Lake Analytics
Why wrong: Azure Data Lake Analytics is a deprecated batch analytics service replaced by Azure Synapse Analytics and Azure Databricks; it is not suitable for real-time streaming.
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 a serverless, real-time stream processing engine designed to handle high-velocity data from sources like IoT devices. It can ingest data from Azure Event Hubs or IoT Hub, apply SQL-based queries to detect anomalies in near real-time, and output results directly to Azure Data Lake Storage for long-term analytics. This makes it the correct choice for the described near-real-time anomaly detection and alerting requirement.
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 Data Factory
Why it's wrong here
Azure Data Factory is an orchestration service for batch data pipelines, not designed for real-time stream processing.
✓
Azure Stream Analytics
Why this is correct
Azure Stream Analytics is the correct service for real-time stream processing of IoT data, with built-in support for windowing, aggregations, and anomaly detection.
Related concept
Read the scenario before looking for a memorised answer.
✗
Azure Analysis Services
Why it's wrong here
Azure Analysis Services is an OLAP engine for creating semantic data models and does not handle real-time stream processing.
✗
Azure Data Lake Analytics
Why it's wrong here
Azure Data Lake Analytics is a deprecated batch analytics service replaced by Azure Synapse Analytics and Azure Databricks; it is not suitable for real-time streaming.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse Azure Data Factory's ability to copy data from streaming sources (like Event Hubs) with actual stream processing, failing to recognize that Data Factory lacks the real-time query and windowing capabilities required for anomaly detection.
Detailed technical explanation
How to think about this question
Under the hood, Azure Stream Analytics uses a temporal windowing mechanism (e.g., tumbling, hopping, or sliding windows) to aggregate and analyze streaming data over time intervals, enabling pattern detection like sudden temperature spikes. It leverages a SQL-like query language with built-in functions for time-series analysis, and its output can be partitioned to multiple sinks, including Azure Data Lake Storage Gen2, with exactly-once delivery semantics. In a real-world scenario, a manufacturing plant might use Stream Analytics to process thousands of sensor readings per second, triggering alerts when vibration levels exceed a threshold within a 5-second sliding 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
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
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 a serverless, real-time stream processing engine designed to handle high-velocity data from sources like IoT devices. It can ingest data from Azure Event Hubs or IoT Hub, apply SQL-based queries to detect anomalies in near real-time, and output results directly to Azure Data Lake Storage for long-term analytics. This makes it the correct choice for the described near-real-time anomaly detection and alerting requirement.
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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