Question 785 of 820
DP-900 Describe an analytics workload on Azure Practice Question
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?
⚠ Common exam trap
Watch out — 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.
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 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.
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 building batch data pipelines, not a real-time stream processing engine. It uses linked services, datasets, and activities to move and transform data on a schedule or via tumbling window triggers, but it cannot ingest and process unbounded, low-latency events continuously. For real-time IoT streaming, ADF is often used only to orchestrate a pipeline that calls Azure Stream Analytics, not to perform the stream processing itself.
- ✓
Azure Stream Analytics
Why this is correct
Azure Stream Analytics is a fully managed, serverless stream processing engine designed exactly for low-latency analysis of IoT device data. It natively supports temporal windowing (tumbling, hopping, sliding, and session windows), event ordering, aggregations, and built-in anomaly detection, and it can take inputs directly from Azure IoT Hub or Event Hubs and write results to SQL Database, Data Lake Storage, Power BI, or downstream event hubs. Because it operates continuously on unbounded streams, it is the correct choice for processing IoT telemetry in real time.
- ✗
Azure Analysis Services
Why it's wrong here
Azure Analysis Services is an OLAP semantic modeling service used to create and host tabular or multidimensional data models for business intelligence reporting. It loads preprocessed, finite datasets from relational sources or data warehouses into memory and serves fast queries via DAX or MDX, but it has no ability to consume live event streams or perform streaming aggregations. Its role is analytical modeling and reporting, not real-time stream ingestion.
- ✗
Azure Data Lake Analytics
Why it's wrong here
Azure Data Lake Analytics was a scalable batch analytics service that used U-SQL to run distributed queries over data already stored in Azure Data Lake Storage. It is now deprecated and replaced by Azure Synapse Analytics and Azure Databricks, and it operated on bounded, stored datasets with job-based execution rather than on continuous, low-latency event streams. Even before deprecation, it was not designed for real-time IoT streaming scenarios.
Quick reference
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
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Last reviewed: Jun 11, 2026
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