Explanation
transactionsDf.dropna(thresh=4)
Correct. Note that by only working with the thresh keyword argument, the first how keyword argument is ignored. Also, figuring out which value to set for thresh can be difficult, especially when under pressure in the exam. Here, I recommend you use the notes to create a "simulation" of what different values for thresh would do to a DataFrame. Here is an explanatory image why thresh=4 is the correct answer to the question:
transactionsDf.dropna(thresh=2)
Almost right. See the comment about thresh for the correct answer above.
transactionsDf.dropna("any")
No, this would remove all rows that have at least one missing value.
transactionsDf.drop.na("",2)
No, drop.na is not a proper DataFrame method.
transactionsDf.dropna("",4)
No, this does not work and will throw an error in Spark because Spark cannot understand the first argument.
More info: pyspark.sql.DataFrame.dropna - PySpark 3.1.1 documentation (https://bit.ly/2QZpiCp) Static notebook | Dynamic notebook: See test 1 (https://flrs.github.io/spark_practice_tests_code/#1/20.html ,
https://bit.ly/sparkpracticeexams_import_instructions)