Databricks Databricks-Certified-Professional-Data-Engineer Exam With Confidence Using Practice Dumps

Send all users notice that the schema for the table will be changing; include in the communication the logic necessary to revert the new table schema to match historic queries.

Configure a new table with all the requisite fields and new names and use this as the source for the customer-facing application; create a view that maintains the original data schema and table name by aliasing select fields from the new table.

Create a new table with the required schema and new fields and use Delta Lake's deep clone functionality to sync up changes committed to one table to the corresponding table.

Replace the current table definition with a logical view defined with the query logic currently writing the aggregate table; create a new table to power the customer-facing application.

Add a table comment warning all users that the table schema and field names will be changing on a given date; overwrite the table in place to the specifications of the customer-facing application.

Because the vacuum command permanently deletes all files containing deleted records, deleted records may be accessible with time travel for around 24 hours.

Because the default data retention threshold is 24 hours, data files containing deleted records will be retained until the vacuum job is run the following day.

Because Delta Lake time travel provides full access to the entire history of a table, deleted records can always be recreated by users with full admin privileges.

Because Delta Lake's delete statements have ACID guarantees, deleted records will be permanently purged from all storage systems as soon as a delete job completes.

Because the default data retention threshold is 7 days, data files containing deleted records will be retained until the vacuum job is run 8 days later.

Isolating tables in separate databases based on data quality tiers allows for easy permissions management through database ACLs and allows physical separation of default storage locations for managed tables.

Because databases on Databricks are merely a logical construct, choices around database organization do not impact security or discoverability in the Lakehouse.

Storinq all production tables in a single database provides a unified view of all data assets available throughout the Lakehouse, simplifying discoverability by granting all users view privileges on this database.

Working in the default Databricks database provides the greatest security when working with managed tables, as these will be created in the DBFS root.

Because all tables must live in the same storage containers used for the database they're created in, organizations should be prepared to create between dozens and thousands of databases depending on their data isolation requirements.