PySpark and AWS: Master Big Data with PySpark and AWS - Glue Job (CDC)

What is the primary purpose of iterating over a data frame?

• To update column names

• To delete rows

• To collect data into a list format

• To change data types

Which action is performed when the row action is 'I'?

• Ignore the row

• Insert the row

• Delete the row

• Update the row

How does the deletion process filter out rows?

• By checking the row's action type

• By comparing row IDs

• By updating the row's status

• By changing the row's data type

What is the result of filtering rows based on unique IDs?

• Only relevant rows are filtered out

• All rows are updated

• All rows are deleted

• No rows are affected

What is the first step in handling insertions?

• Convert the row into a list

• Delete existing rows

• Update the data frame schema

• Change the data type of columns

What is the purpose of creating a list of columns during insertion?

• To change data types

• To update column names

• To delete columns

• To create a new data frame

What is a key challenge in updating rows?

• Identifying the correct row to update

• Deleting the row after update

• Ignoring the row if it is not found

• Changing the data type of the row

What happens if the row ID matches during an update?

• The row is ignored

• The row is inserted

• The row's data is updated

• The row is deleted

What is the significance of the 'otherwise' clause in updates?

• It updates the row

• It deletes the row

• It changes the data type

• It retains existing data if conditions are not met

What is the final step after handling all actions on data rows?

• Change the data frame schema

• Ignore the changes

• Save the updated data frame to the destination

• Delete the data frame