Databricks Certified Data Engineer - Associate
Get started today
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?
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
A
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))
```*_
31.7%
B
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))
```*_
Comments
Loading comments...