Google Cloud Professional Data Engineer Exam 2025 - Free Practice Questions and Study Guide

The choice of Cloud Pub/Sub as the technology used to build data pipelines in Google Cloud is particularly appropriate due to its capabilities as a messaging service that facilitates asynchronous communication between different components of a data pipeline. Cloud Pub/Sub allows for the ingestion of large volumes of data in real-time, enabling systems to send and receive messages reliably. This is crucial for data processing tasks, where data needs to be moved between various services or applications seamlessly without tight coupling.

In the context of data pipelines, Cloud Pub/Sub excels because it operates on a publish-subscribe model. This architecture allows data producers (publishers) to push messages to the service without knowing who the consumers (subscribers) are, and vice versa. This decoupling is essential for scalability and flexibility when building data workflows, as it allows you to create dynamic pipelines that can adjust to various data sources and processing tasks effectively.

Other technologies listed, such as Cloud Asset Inventory, AI Platform, and Cloud Functions, serve different purposes within the Google Cloud ecosystem. Cloud Asset Inventory is more focused on resource management and visibility rather than data pipeline construction. AI Platform is designed for machine learning tasks, enabling the training and deployment of models, which is distinct from managing the flow of data. Cloud Functions provides a server