Google Cloud Professional Data Engineer Exam 2025 – 400 Free Practice Questions to Pass the Exam

Google Cloud Dataflow primarily assists with real-time big data processing, which involves the ability to handle and analyze large volumes of continuously streaming data as well as processing batch data. It is built on the Apache Beam programming model, enabling developers to create data processing jobs that can run in parallel and scale efficiently with the volume and velocity of incoming data.

Dataflow supports event-driven architectures and allows for the development of pipelines that can respond to changing data in real-time. This means that as new data arrives, it can be processed and analyzed immediately, which is crucial for applications such as real-time analytics, fraud detection, and monitoring.

The other options do not accurately represent the main function of Dataflow. Static data storage typically refers to services like Google Cloud Storage or BigQuery, which are designed for storing and querying data rather than processing it in real-time. Document management tends to involve organizing and managing documents within a system, not processing streams of data. Mobile application development is separate from data processing and relates more to building apps for mobile devices rather than handling big data workloads.