DP-203 Develop data processing Practice Question
You are designing a data pipeline in Azure Synapse Analytics that ingests streaming taxi trip data from Azure Event Hubs. The data must be processed in near real-time and stored in a dedicated SQL pool. The pipeline should handle late-arriving data (up to 30 minutes late) without reprocessing the entire stream. Which Azure service should you use to process the streaming data?
⚠ Common exam trap
Many exam-takers confuse Azure Data Factory's 'real-time' monitoring or Azure Functions' 'event-driven' nature with true stream processing, overlooking the need for built-in windowing and late-arrival handling that only Azure Stream Analytics provides.
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 the correct choice because it is designed for real-time stream processing with native support for Event Hubs as an input and dedicated SQL pool as an output. It can handle late-arriving data via its built-in 'late arrival' window (configurable up to 30 minutes) using event time processing, without requiring reprocessing of the entire stream.
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 Databricks Structured Streaming
Why it's wrong here
Azure Databricks Structured Streaming, while a powerful engine for real-time stream processing and handling late-arriving data with watermarking, is not the most integrated service for directly ingesting streaming data into an Azure Synapse dedicated SQL pool. Its strength lies in complex transformations, machine learning integration, or writing to Delta Lake. This option is tempting because it excels at processing streaming data with advanced windowing for late arrivals, making it suitable when the pipeline requires extensive custom code or targets a Delta Lake architecture before Synapse ingestion.
- ✓
Azure Stream Analytics
Why this is correct
Azure Stream Analytics provides built-in support for late-arriving events and can output directly to Synapse SQL pool.
- ✗
Azure Data Factory
Why it's wrong here
Azure Data Factory is an orchestration service for batch data movement and transformation, not real-time stream processing.
- ✗
Azure Functions with Event Hubs trigger
Why it's wrong here
Azure Functions can process events but requires custom code for late-arriving data and windowing.
Go deeper
Related to this question
Learn chapter
Introduction to Azure Data Engineering
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
Azure Synapse Analytics
Azure Synapse Analytics is a cloud-based data integration, warehousing, and analytics service that brings together big data and data warehouse capabilities under one platform.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This DP-203 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-203 exam.