Explanation
transactionsDf.unpersist()
Correct. The DataFrame.unpersist() command does exactly what the question asks for - it removes all cached parts of the DataFrame from memory and disk.
del transactionsDf
False. While this option can help remove the DataFrame from memory and disk, it does not do so immediately. The reason is that this command just notifies the Python garbage collector that the transactionsDf now may be deleted from memory. However, the garbage collector does not do so immediately and, if you wanted it to run immediately, would need to be specifically triggered to do so. Find more information linked below.
array_remove(transactionsDf, "*")
Incorrect. The array_remove method from pyspark.sql.functions is used for removing elements from arrays in columns that match a specific condition. Also, the first argument would be a column, and not a DataFrame as shown in the code block.
transactionsDf.persist()
No. This code block does exactly the opposite of what is asked for: It caches (writes) DataFrame transactionsDf to memory and disk. Note that even though you do not pass in a specific storage level here, Spark will use the default storage level (MEMORY_AND_DISK).
transactionsDf.clearCache()
Wrong. Spark's DataFrame does not have a clearCache() method.
More info: pyspark.sql.DataFrame.unpersist - PySpark 3.1.2 documentation, python - How to delete an RDD in PySpark for the purpose of releasing resources? - Stack Overflow Static notebook | Dynamic notebook: See test 3