What does schema evolution allow a Delta Lake to do?

Schema evolution in Delta Lake allows the structure of a table to adapt automatically as data is modified, particularly when new columns are added or existing columns are modified. This is especially useful in dynamic environments where the data model may change frequently, enabling datasets to evolve without requiring complex migration scripts or manual adjustments. For instance, if a new column with a specific data type is introduced in incoming data, schema evolution can seamlessly integrate this new column into the existing schema of the Delta table.

The process increases flexibility in managing evolving datasets while ensuring that existing data remains accessible and usable. This capability is essential for data engineering tasks where agility and adaptability are key to responding to changing data requirements.

The other choices do not accurately capture the benefits of schema evolution. Ignoring unknown data types, retaining data without changes, and automatically backing up data do not pertain to the core functionality of schema evolution in Delta Lake. Instead, they describe separate features or actions that do not directly relate to how Delta Lake handles schema changes during data ingestion or modification.