Which of the following describes how Databricks handles data storage?

Databricks is designed to be a cloud-agnostic platform, which means it is built to integrate seamlessly with multiple cloud storage solutions. This flexibility allows users to leverage the storage capabilities of major cloud providers such as AWS, Azure, and Google Cloud. By relying on these cloud storage systems, Databricks can efficiently manage large datasets and benefit from the scalability, durability, and performance that cloud storage offers.

Utilizing cloud storage allows Databricks to support a variety of data formats and provide users with the ability to access and analyze their data from anywhere. This architecture not only enhances collaboration but also aligns with modern data engineering practices that prioritize cloud-based solutions for analytics and machine learning tasks.

In contrast to the other options, relying solely on local storage limits scalability and accessibility, on-premises databases restrict the flexibility of deployment, and permanently storing all data within its own platform is not aligned with the general practice of using external storage solutions for data management in modern cloud-based architectures.