DP-900 Describe an analytics workload on Azure Practice Question
A data engineering team needs to build a pipeline that ingests streaming data from IoT devices into Azure Data Lake Storage Gen2. The data arrives as JSON messages. They want to use a service that can capture the streaming data in near real-time and store it as files in the data lake without writing custom code for the ingestion. Which Azure service should they use?
⚠ Common exam trap
It's easy for candidates to confuse Azure Stream Analytics as the primary ingestion service for raw data capture, when in fact Stream Analytics is a processing engine that requires a query and output sink, whereas Event Hubs Capture provides direct, code-free persistence of raw streams.
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 Event Hubs with Capture
Azure Event Hubs with Capture is the correct choice because it natively ingests streaming JSON data from IoT devices in near real-time and automatically writes the data to Azure Data Lake Storage Gen2 as files without requiring any custom code. The Capture feature automatically persists the event stream to the specified storage destination at defined time or size intervals, making it ideal for serverless, code-free ingestion.
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 a batch-oriented orchestration service that moves and transforms data at rest. It relies on scheduled pipelines, tumbling windows, and copy activities that poll sources, making it fundamentally incapable of continuously capturing a live event stream. To work with streaming data, an engineer would first need a separate streaming ingestion service (such as Event Hubs) to land events in storage, then Data Factory could later copy or transform those files — but that is not native streaming ingestion.
- ✓
Azure Event Hubs with Capture
Why this is correct
Azure Event Hubs with Capture is a fully managed, real-time streaming ingestion service that natively persists raw event data to Azure Blob Storage or Azure Data Lake Storage Gen2 without any custom code. Capture automatically writes the incoming event stream to files in Avro format based on user-defined time or size intervals (for example, every 15 minutes or when 100 MB accumulates), providing a near-real-time, durable archive of the raw stream. This exactly satisfies the requirement of ingesting and capturing streaming data directly into storage.
- ✗
Azure Stream Analytics
Why it's wrong here
Stream Analytics is used for real-time processing and analytics (e.g., aggregations, pattern matching). It is not designed solely for capturing raw streaming data into files; it would require additional configuration and still involve processing logic.
- ✗
Azure Synapse Pipelines
Why it's wrong here
Azure Synapse Pipelines are built on the same engine as Azure Data Factory and therefore inherit the same batch-oriented, schedule-driven architecture. They are designed to orchestrate and transform data already stored in a data lake or warehouse, not to attach to a continuous event feed. Without first using a dedicated streaming service (such as Event Hubs with Capture) to materialize events into storage, Synapse Pipelines cannot ingest streaming data natively; doing so would require custom code or an external source, making it an incorrect fit for this scenario.
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
Data lake
A data lake is a centralized storage repository that holds vast amounts of raw data in its native format until it is needed for analysis.
Key term
Pipeline
A pipeline is an automated series of steps that takes code from development to production, ensuring quality and speed.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.