Data Engineering Associate with Databricks Practice Exam - Prep Guide & Practice Test

The function that risks failing if the schema changes is the option "INSERT OVERWRITE." This operation is designed to replace existing data in a table with new data. When using INSERT OVERWRITE, the operation expects the data being inserted to conform to the schema already defined in the target table.

If the schema of the target table changes — for instance, if columns are added, removed, or have altered data types — it can result in a mismatch when attempting to insert the new data. This could lead to errors during the execution of the operation because the database expects the incoming data to align perfectly with the defined schema.

In contrast, other functions such as INSERT INTO and MERGE INTO may handle schema changes differently. For instance, MERGE INTO is designed to update or insert records based on specified conditions, and it can accommodate changes in the dataset's schema more gracefully. COPY INTO is typically used for loading data from files into a table and often has mechanisms for handling schema mismatches, once again making it less susceptible to schema changes.

Thus, when considering the risk associated with schema changes, INSERT OVERWRITE is particularly vulnerable as it directly replaces the content based on the expected schema without the flexibility to adapt to modifications.