Explanation
Output of correct code block:
+----------------------------------+------+
|itemName |col |
+----------------------------------+------+
|Thick Coat for Walking in the Snow|blue |
|Thick Coat for Walking in the Snow|winter|
|Thick Coat for Walking in the Snow|cozy |
|Outdoors Backpack |green |
|Outdoors Backpack |summer|
|Outdoors Backpack |travel|
+----------------------------------+------+
The key to solving this question is knowing about Spark's explode operator. Using this operator, you can extract values from arrays into single rows. The following guidance steps through the answers systematically from the first to the last gap. Note that there are many ways to solving the gap questions and filtering out wrong answers, you do not always have to start filtering out from the first gap, but can also exclude some answers based on obvious problems you see with them.
The answers to the first gap present you with two options: filter and where. These two are actually synonyms in PySpark, so using either of those is fine. The answer options to this gap therefore do not help us in selecting the right answer.
The second gap is more interesting. One answer option includes "Sports".isin(col("Supplier")). This construct does not work, since Python's string does not have an isin method. Another option contains col(supplier). Here, Python will try to interpret supplier as a variable. We have not set this variable, so this is not a viable answer. Then, you are left with answers options that include col ("supplier").contains("Sports") and col("supplier").isin("Sports"). The question states that we are looking for suppliers whose name includes Sports, so we have to go for the contains operator here.
We would use the isin operator if we wanted to filter out for supplier names that match any entries in a list of supplier names.
Finally, we are left with two answers that fill the third gap both with "itemName" and the fourth gap either with explode("attributes") or "attributes". While both are correct Spark syntax, only explode ("attributes") will help us achieve our goal. Specifically, the question asks for one attribute from column attributes per row - this is what the explode() operator does.
One answer option also includes array_explode() which is not a valid operator in PySpark.
More info: pyspark.sql.functions.explode - PySpark 3.1.2 documentation
Static notebook | Dynamic notebook: See test 3