PySpark and AWS: Master Big Data with PySpark and AWS - Spark DF (UDFs)

The video tutorial explains how to use user-defined functions (UDFs) in data frames. It covers creating a new column for total salary by summing salary and bonus, writing and registering a UDF, specifying parameters and return types, and applying UDFs to manipulate data frame columns. The tutorial emphasizes best practices and provides examples of using UDFs for data manipulation.

What is the primary purpose of using User Defined Functions (UDFs) in data frames?

• To create new data frames

• To manipulate and transform column values

• To delete columns from data frames

• To sort data frames

Which two columns are used to calculate the total salary in the UDF example?

• Salary and Department

• Bonus and Age

• Department and State

• Salary and Bonus

Why is it important to specify the return type when registering a UDF?

• To increase the speed of the UDF

• To allow the UDF to handle multiple columns

• To make the UDF compatible with all data frames

• To ensure the UDF returns the correct data type

What does the UDF do with each row of the data frame?

• It ignores the row

• It processes each element of the row

• It deletes the row

• It duplicates the row

What is a good practice when specifying the return type of a UDF?

• Always use string type

• Use the default type without specifying

• Never specify the return type

• Specify the return type even if it is a string

How can you increase a salary column by a fixed amount using a UDF?

• By adding a constant value to the salary

• By subtracting a constant value from the salary

• By multiplying the salary by a factor

• By dividing the salary by a constant

What flexibility do UDFs offer in terms of column manipulation?

• They allow for the creation and deletion of columns

• They only allow for column deletion

• They do not allow any column manipulation

• They only allow for column creation