Databricks Certified Data Engineer - Associate

Ultimate access to all questions.

A data engineer needs to create a PySpark DataFrame transformation that applies a custom function to a column of integers called sales in order to return the square of each value. Which of the following code blocks correctly defines and applies a Spark UDF to accomplish this task?

Exam-Like

def square(x):
    return x * x

square_udf = udf(square, IntegerType())
df = df.withColumn('sales_squared', square_udf(df.sales))

def square(x):
    return x * x

square_udf = udf(square, IntegerType())
df = df.withColumn('sales_squared', square_udf(df.sales))

31.9%

def square(x):
    return x * x

df = df.withColumn('sales_squared', square(df.sales))

def square(x):
    return x * x

df = df.withColumn('sales_squared', square(df.sales))

Loading comments...

def square(x):
    return x * x

df = df.withColumn('sales_squared', udf(square, IntegerType())('sales'))

def square(x):
    return x * x

df = df.withColumn('sales_squared', udf(square, IntegerType())('sales'))

10.0%

def square(x):
    return x * x

square_udf = udf(square, IntegerType)
df = df.withColumn('sales_squared', square_udf('sales'))

def square(x):
    return x * x

square_udf = udf(square, IntegerType)
df = df.withColumn('sales_squared', square_udf('sales'))

15.0%

def square(x):
    return x ** 2

square_udf = udf(square, IntegerType())
df = df.withColumn('sales_squared', square_udf('sales'))

def square(x):
    return x ** 2

square_udf = udf(square, IntegerType())
df = df.withColumn('sales_squared', square_udf('sales'))

31.0%