Which of the following code blocks returns a DataFrame with an added column to DataFrame transactionsDf that shows the unix epoch timestamps in column transactionDate as strings in the format month/day/year in column transactionDateFormatted?
Excerpt of DataFrame transactionsDf:

• A. transactionsDf.withColumn("transactionDateFormatted", from_unixtime("transactionDate"))
• B. transactionsDf.withColumn("transactionDateFormatted", from_unixtime("transactionDate", format="dd/MM/yyyy"))
• C. transactionsDf.withColumn("transactionDateFormatted", from_unixtime("transactionDate", format="MM/dd/yyyy"))
• D. transactionsDf.apply(from_unixtime(format="MM/dd/yyyy")).asColumn("transactionDateFormatted")
• E. transactionsDf.withColumnRenamed("transactionDate", "transactionDateFormatted", from_unixtime("transactionDateFormatted", format="MM/dd/yyyy"))

The code block displayed below contains an error. The code block should return the average of rows in column value grouped by unique storeId. Find the error.
Code block:
transactionsDf.agg("storeId").avg("value")

• A. The avg("value") should be specified as a second argument to agg() instead of being appended to it.
• B. agg should be replaced by groupBy.
• C. All column names should be wrapped in col() operators.
• D. Instead of avg("value"), avg(col("value")) should be used.
• E. "storeId" and "value" should be swapped.

The code block displayed below contains an error. The code block should merge the rows of DataFrames transactionsDfMonday and transactionsDfTuesday into a new DataFrame, matching column names and inserting null values where column names do not appear in both DataFrames. Find the error.
Sample of DataFrame transactionsDfMonday:
1.+-------------+---------+-----+-------+---------+----+
2.|transactionId|predError|value|storeId|productId| f|
3.+-------------+---------+-----+-------+---------+----+
4.| 5| null| null| null| 2|null|
5.| 6| 3| 2| 25| 2|null|
6.+-------------+---------+-----+-------+---------+----+
Sample of DataFrame transactionsDfTuesday:
1.+-------+-------------+---------+-----+
2.|storeId|transactionId|productId|value|
3.+-------+-------------+---------+-----+
4.| 25| 1| 1| 4|
5.| 2| 2| 2| 7|
6.| 3| 4| 2| null|
7.| null| 5| 2| null|
8.+-------+-------------+---------+-----+
Code block:
sc.union([transactionsDfMonday, transactionsDfTuesday])

• A. Instead of the Spark context, transactionDfMonday should be called with the union method.
• B. Instead of union, the concat method should be used, making sure to not use its default arguments.
• C. The DataFrames' RDDs need to be passed into the sc.union method instead of the DataFrame variable names.
• D. Instead of the Spark context, transactionDfMonday should be called with the unionByName method instead of the union method, making sure to not use its default arguments.
• E. Instead of the Spark context, transactionDfMonday should be called with the join method instead of the union method, making sure to use its default arguments.

Which of the following code blocks stores DataFrame itemsDf in executor memory and, if insufficient memory is available, serializes it and saves it to disk?

• A. itemsDf.persist(StorageLevel.MEMORY_ONLY)
• B. itemsDf.cache(StorageLevel.MEMORY_AND_DISK)
• C. itemsDf.write.option('destination', 'memory').save()
• D. itemsDf.store()
• E. itemsDf.cache()