Explanation
Correct code block:
articlesDf = articlesDf.select(explode(col('attributes')))
articlesDf = articlesDf.groupby('col').count()
articlesDf = articlesDf.sort('count',ascending=False).select('col')
Output of correct code block:
+-------+
| col|
+-------+
| summer|
| winter|
| blue|
| cozy|
| travel|
| fresh|
| red|
|cooling|
| green|
+-------+
Static notebook | Dynamic notebook: See test 2

Explanation
Correct code block:
itemsDf.join(transactionsDf, itemsDf.itemId==transactionsDf.transactionId) The join statement is incomplete.
Correct! If you look at the documentation of DataFrame.join() (linked below), you see that the very first argument of join should be the DataFrame that should be joined with. This first argument is missing in the code block.
The join method is inappropriate.
No. By default, DataFrame.join() uses an inner join. This method is appropriate for the scenario described in the question.
The join expression is malformed.
Incorrect. The join expression itemsDf.itemId==transactionsDf.transactionId is correct syntax.
The merge method should be used instead of join.
False. There is no DataFrame.merge() method in PySpark.
The union method should be used instead of join.
Wrong. DataFrame.union() merges rows, but not columns as requested in the question.
More info: pyspark.sql.DataFrame.join - PySpark 3.1.2 documentation, pyspark.sql.DataFrame.union - PySpark 3.1.2 documentation Static notebook | Dynamic notebook: See test 3

Explanation
Correct code block:
importedDf = spark.read.json(jsonPath)
importedDf.createOrReplaceTempView("importedDf")
spark.sql("SELECT * FROM importedDf WHERE productId != 3")
Option 5 is the only correct way listed of reading in a JSON in PySpark. The option("format", "json") is not the correct way to tell Spark's DataFrameReader that you want to read a JSON file. You would do this through format("json") instead. Also, you can communicate the specific path of the JSON file to the DataFramReader using the load() method, not the path() method.
In order to use a SQL command through the SparkSession spark, you first need to create a temporary view through DataFrame.createOrReplaceTempView().
The SQL statement should start with the SELECT operator. The FILTER operator SQL provides is not the correct one to use here.
Static notebook | Dynamic notebook: See test 2

Explanation
This is a very tricky question and involves both knowledge about merging as well as schemas when reading parquet files.
spark.read.option("mergeSchema", "true").parquet(filePath)
Correct. Spark's DataFrameReader's mergeSchema option will work well here, since columns that appear in both partitions have matching data types. Note that mergeSchema would fail if one or more columns with the same name that appear in both partitions would have different data types.
spark.read.parquet(filePath)
Incorrect. While this would read in data from both partitions, only the schema in the parquet file that is read in first would be considered, so some columns that appear only in the second partition (e.g. tax_id) would be lost.
nx = 0
for file in dbutils.fs.ls(filePath):
if not file.name.endswith(".parquet"):
continue
df_temp = spark.read.parquet(file.path)
if nx == 0:
df = df_temp
else:
df = df.union(df_temp)
nx = nx+1
df
Wrong. The key idea of this solution is the DataFrame.union() command. While this command merges all data, it requires that both partitions have the exact same number of columns with identical data types.
spark.read.parquet(filePath, mergeSchema="y")
False. While using the mergeSchema option is the correct way to solve this problem and it can even be called with DataFrameReader.parquet() as in the code block, it accepts the value True as a boolean or string variable. But 'y' is not a valid option.
nx = 0
for file in dbutils.fs.ls(filePath):
if not file.name.endswith(".parquet"):
continue
df_temp = spark.read.parquet(file.path)
if nx == 0:
df = df_temp
else:
df = df.join(df_temp, how="outer")
nx = nx+1
df
No. This provokes a full outer join. While the resulting DataFrame will have all columns of both partitions, columns that appear in both partitions will be duplicated - the question says all columns that are included in the partitions should appear exactly once.
More info: Merging different schemas in Apache Spark | by Thiago Cordon | Data Arena | Medium Static notebook | Dynamic notebook: See test 3

Explanation
Correct code block:
transactionsDf.join(broadcast(itemsDf), "transactionId", "left_semi")
This question is extremely difficult and exceeds the difficulty of questions in the exam by far.
A first indication of what is asked from you here is the remark that "the query should be executed in an optimized way". You also have qualitative information about the size of itemsDf and transactionsDf. Given that itemsDf is "very small" and that the execution should be optimized, you should consider instructing Spark to perform a broadcast join, broadcasting the "very small" DataFrame itemsDf to all executors. You can explicitly suggest this to Spark via wrapping itemsDf into a broadcast() operator. One answer option does not include this operator, so you can disregard it. Another answer option wraps the broadcast() operator around transactionsDf - the bigger of the two DataFrames. This answer option does not make sense in the optimization context and can likewise be disregarded.
When thinking about the broadcast() operator, you may also remember that it is a method of pyspark.sql.functions. One answer option, however, resolves to itemsDf.broadcast([...]). The DataFrame class has no broadcast() method, so this answer option can be eliminated as well.
All two remaining answer options resolve to transactionsDf.join([...]) in the first 2 gaps, so you will have to figure out the details of the join now. You can pick between an outer and a left semi join. An outer join would include columns from both DataFrames, where a left semi join only includes columns from the "left" table, here transactionsDf, just as asked for by the question. So, the correct answer is the one that uses the left_semi join.