What is the characteristic of the Change Data Feed in Databricks with respect to delete operations?

The characteristic of the Change Data Feed (CDF) in Databricks that pertains to delete operations is that it facilitates consistent delete operations across tables. This feature allows users to track changes in data effectively, providing a reliable mechanism for identifying which records have been deleted.

The CDF captures changes to data, including both INSERT and DELETE actions, and presents them in a manner that can be easily consumed by downstream processes or analytics. This capability is crucial for maintaining data integrity, especially in environments where multiple tables may interrelate. By leveraging the CDF, users can ensure that when a record is deleted in one table, operations on related tables can also respond appropriately, maintaining the overall consistency of the dataset.

In contrast, while the other options touch upon aspects of data management and foreign keys, they do not accurately reflect the primary function of the CDF concerning delete operations. For example, the CDF does not specifically create temporal records for deleted rows in the traditional sense, nor does it eliminate the need for foreign key relationships; these relationships may still be relevant in maintaining data integrity in other contexts.