DP-900 Describe an analytics workload on Azure Practice Question
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?
⚠ Common exam trap
It's easy for candidates to confuse Azure Data Factory's batch orchestration capabilities with real-time processing, or mistakenly think Azure Data Lake Analytics can handle streaming data because of its 'analytics' name, but neither supports continuous, low-latency 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
✓
B. Azure Stream Analytics
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.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
A. Azure Data Factory
Why it's wrong here
Azure Data Factory is a managed ETL and data orchestration service that moves and transforms data on a schedule or via event triggers, but it processes data in batches rather than as an unbounded real-time stream. It does not have native stream-processing operators like tumbling or hopping windows, so it cannot evaluate a continuous GPS feed for conditions such as geofencing or speed thresholds in real time. While it can copy data near the source, its latency and pull-based model make it unsuitable for real-time alerting on live telemetry.
- ✓
B. Azure Stream Analytics
Why this is correct
Azure Stream Analytics is a fully managed, PaaS stream-processing engine that consumes data from sources such as Event Hubs, IoT Hub, or Blob storage and applies SQL-like queries with temporal windows—tumbling, hopping, sliding, and session—to filter, aggregate, and join events. It processes millions of events per second with low latency and can emit alert outputs directly to Power BI, Azure Functions, or Event Hubs, making it ideal for real-time GPS geofencing, speed alerts, or route analytics. Stream Analytics also supports exactly-once event delivery and watermarking for handling late or out-of-order telemetry, and it can run on IoT Edge for on-device processing.
- ✗
C. Azure Data Lake Analytics
Why it's wrong here
Azure Data Lake Analytics is a batch analytics service that runs U-SQL jobs over static files in Data Lake Storage or Blob, making it suitable for historical exploration and large-scale offline processing but not for low-latency event processing. Each job has scheduling overhead, and because it loads files at query time, it cannot subscribe to a live GPS event stream or react to individual location pings in real time. It lacks built-in windowing or event-time primitives, so it cannot produce immediate alerts based on a continuously arriving data feed.
- ✗
D. Azure Analysis Services
Why it's wrong here
Azure Analysis Services provides enterprise-grade semantic modeling with an in-memory tabular engine that serves pre-aggregated data to Power BI, Excel, and other BI clients, and its data is refreshed on a schedule or manually, not ingested as an event stream. It cannot subscribe to a real-time GPS topic or evaluate conditions event-by-event; DirectQuery may push some queries back to an external source, but AAS itself does no stream processing or alerting. Therefore, it is meant for interactive analytical queries on already-prepared data, not for turning live telemetry into immediate actions.
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 |
Go deeper
Related to this question
Learn chapter
Data Roles and Core Concepts
Key term
Azure Stream Analytics
Azure Stream Analytics is a fully managed, real-time data processing service that analyzes and transforms high volumes of streaming data from various sources to deliver low-latency insights and trigger actions.
Key term
Stream processing
Stream processing is a data processing method that continuously analyzes and acts on data in real time as it arrives, rather than storing it first and processing it later.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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