Which of the following code blocks returns a 2-column DataFrame that shows the distinct values in column productId and the number of rows with that…

transactionsDf.groupBy("productId").count()

Correct. This code block first groups DataFrame transactionsDf by column productId and then counts

the rows in each group.

transactionsDf.groupBy("productId").select(count("value"))

Incorrect. You cannot call select on a GroupedData object (the output of a groupBy) statement.

transactionsDf.count("productId")

No. DataFrame.count() does not take any arguments.

transactionsDf.count("productId").distinct()

Wrong. Since DataFrame.count() does not take any arguments, this option cannot be right.

transactionsDf.groupBy("productId").agg(col("value").count())

False. A Column object, as returned by col("value"), does not have a count() method. You can see all

available methods for Column object linked in the Spark documentation below.

More info: pyspark.sql.DataFrame.count — PySpark 3.1.2 documentation, pyspark.sql.Column —

PySpark 3.1.2 documentation

Static notebook | Dynamic notebook: See test 3,