Match each Azure data integration tool to its typical use case.
Drag a concept onto its matching description — or click a concept then click the description.
Query external data in Azure Storage using T-SQL
High-throughput data ingestion into Synapse SQL
Orchestrate data movement and transformation
Complex data engineering with notebooks
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Azure Data Factory: Orchestrate and automate data movement and transformation at scale.
In this matching exercise, the correct pairs are: Azure Data Factory for orchestration, Azure Synapse Analytics for data warehousing, Azure Databricks for data engineering/ML, and Azure Stream Analytics for real-time streaming. Common confusions involve swapping orchestration and streaming roles.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Azure Data Factory: Orchestrate and automate data movement and transformation at scale.
Why this is correct
Azure Data Factory is a cloud-based ETL service for orchestrating data pipelines.
Azure Synapse Analytics: Perform large-scale data warehousing and analytics with integrated SQL and Spark.
Why this is correct
Azure Synapse Analytics provides a unified analytics platform for enterprise data warehousing.
Azure Databricks: Run collaborative data engineering and machine learning workloads using Apache Spark.
Why this is correct
Azure Databricks is optimized for data science and engineering collaboration.
Azure Stream Analytics: Process and analyze real-time streaming data from sources like IoT devices.
Why this is correct
Azure Stream Analytics enables real-time data stream processing.
Azure Data Factory: Process real-time streaming data from IoT devices.
Why it's wrong here
Incorrect — real-time streaming is handled by Azure Stream Analytics, not Data Factory.
Azure Synapse Analytics: Orchestrate data movement at scale.
Why it's wrong here
Incorrect — orchestration is the role of Azure Data Factory.
Go deeper
Learn chapter
Introduction to Azure Data Engineering
Key term
ETL vs ELT Design
ETL and ELT are two different ways to move and transform data from source systems into a data warehouse or data lake, differing in where and when the transformation happens.
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.
About these practice questions
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Same concept, more angles
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. Match the Azure service to its primary data processing use case. Drag each service on the left to the correct use case on the right. Services: Azure Databricks, Azure Stream Analytics, Azure Data Factory, Azure Synapse Analytics Use Cases: - Real-time event processing - Orchestration of ETL pipelines - Big data analytics with Spark - Enterprise data warehousing
mediumWhy A: All four matches are correct. Azure Databricks is used for big data analytics with Apache Spark. Azure Stream Analytics processes real-time event streams. Azure Data Factory orchestrates and automates ETL pipelines. Azure Synapse Analytics serves as an enterprise data warehouse with integrated analytics.
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.