Which of the following code blocks immediately removes the previously cached DataFrame transactionsDf from memory and disk?

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,