Explanation
Static notebook | Dynamic notebook: See test 1

Explanation
Correct code block:
itemsDf.withColumn("itemNameElements", split("itemName"," "))
Output of code block:
+------+----------------------------------+-------------------+------------------------------------------+
|itemId|itemName |supplier |itemNameElements |
+------+----------------------------------+-------------------+------------------------------------------+
|1 |Thick Coat for Walking in the Snow|Sports Company Inc.|[Thick, Coat, for, Walking, in, the, Snow]|
|2 |Elegant Outdoors Summer Dress |YetiX |[Elegant, Outdoors, Summer, Dress] |
|3 |Outdoors Backpack |Sports Company Inc.|[Outdoors, Backpack] |
+------+----------------------------------+-------------------+------------------------------------------+ The key to solving this question is that the split method definitely needs a second argument here (also look at the link to the documentation below). Given the values in column itemName in DataFrame itemsDf, this should be a space character " ". This is the character we need to split the words in the column.
More info: pyspark.sql.functions.split - PySpark 3.1.1 documentation
Static notebook | Dynamic notebook: See test 1

The correct code block looks like this:

Then, the first couple of rows of itemAttributesDf look like this:

explode() is not a method of DataFrame. explode() should be used inside the select() method instead.
This is correct.
The split() method should be used inside the select() method instead of the explode() method.
No, the split() method is used to split strings into parts. However, column attributs is an array of strings. In this case, the explode() method is appropriate.
Since itemId is the index, it does not need to be an argument to the select() method.
No, itemId still needs to be selected, whether it is used as an index or not.
The explode() method expects a Column object rather than a string.
No, a string works just fine here. This being said, there are some valid alternatives to passing in a string:

The alias() method needs to be called after the select() method.
No.
More info: pyspark.sql.functions.explode - PySpark 3.1.1 documentation (https://bit.ly/2QUZI1J) Static notebook | Dynamic notebook: See test 1 (https://flrs.github.io/spark_practice_tests_code/#1/22.html ,
https://bit.ly/sparkpracticeexams_import_instructions)

Explanation
The trick in this question is knowing the "modes" of the DataFrameWriter. Mode ignore will ignore if a file already exists and not replace that file, but also not throw an error. Mode errorifexists will throw an error, and is the default mode of the DataFrameWriter. The question NO:
explicitly calls for the DataFrame to be "silently" written if it does not exist, so you need to specify mode("ignore") here to avoid having Spark communicate any error to you if the file already exists.
The `overwrite' mode would not be right here, since, although it would be silent, it would overwrite the already-existing file. This is not what the question asks for.
It is worth noting that the option starting with spark.DataFrameWriter(itemsDf) cannot work, since spark references the SparkSession object, but that object does not provide the DataFrameWriter.
As you can see in the documentation (below), DataFrameWriter is part of PySpark's SQL API, but not of its SparkSession API.
More info:
DataFrameWriter: pyspark.sql.DataFrameWriter.save - PySpark 3.1.1 documentation SparkSession API: Spark SQL - PySpark 3.1.1 documentation Static notebook | Dynamic notebook: See test 1

Explanation
Correct code block:
transactionsDf.withColumnRenamed("transactionId", "transactionNumber")
Note that in Spark, a copy is returned by default. So, there is no need to append copy() to the code block.
More info: pyspark.sql.DataFrame.withColumnRenamed - PySpark 3.1.2 documentation Static notebook | Dynamic notebook: See test 2