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The code block displayed below contains an error. The code block is intended to write DataFrame transactionsDf to disk as a parquet file in location /FileStore/transactions_split, using column storeId as key for partitioning. Find the error.

Code block:

transactionsDf.write.format("parquet").partitionOn("storeId").save("/FileStore/transactions_s plit")A.

A. The format("parquet") expression is inappropriate to use here, "parquet" should be passed as first argument to the save() operator and "/FileStore/transactions_split" as the second argument.

B. Partitioning data by storeId is possible with the partitionBy expression, so partitionOn should be replaced by partitionBy.

C. Partitioning data by storeId is possible with the bucketBy expression, so partitionOn should be replaced by bucketBy.

D. partitionOn("storeId") should be called before the write operation.

E. The format("parquet") expression should be removed and instead, the information should be added to the write expression like so: write("parquet").

The code block displayed below contains multiple errors. The code block should remove column transactionDate from DataFrame transactionsDf and add a column transactionTimestamp in which

dates that are expressed as strings in column transactionDate of DataFrame transactionsDf are converted into unix timestamps. Find the errors.

Sample of DataFrame transactionsDf:

1.+-------------+---------+-----+-------+---------+----+----------------+

2.|transactionId|predError|value|storeId|productId| f| transactionDate| 3.+-------------+---------+-----+-------+---------+----+----------------+

4.| 1| 3| 4| 25| 1|null|2020-04-26 15:35|

5.| 2| 6| 7| 2| 2|null|2020-04-13 22:01|

6.| 3| 3| null| 25| 3|null|2020-04-02 10:53|

7.+-------------+---------+-----+-------+---------+----+----------------+

Code block:

1.transactionsDf = transactionsDf.drop("transactionDate")

2.transactionsDf["transactionTimestamp"] = unix_timestamp("transactionDate", "yyyy-MM- dd")

A. Column transactionDate should be dropped after transactionTimestamp has been written. The string indicating the date format should be adjusted. The withColumn operator should be used instead of the existing column assignment. Operator to_unixtime() should be used instead of unix_timestamp().

B. Column transactionDate should be dropped after transactionTimestamp has been written. The withColumn operator should be used instead of the existing column assignment. Column transactionDate should be wrapped in a col() operator.

C. Column transactionDate should be wrapped in a col() operator.

D. The string indicating the date format should be adjusted. The withColumnReplaced operator should be used instead of the drop and assign pattern in the code block to replace column transactionDate with the new column transactionTimestamp.

E. Column transactionDate should be dropped after transactionTimestamp has been written. The string indicating the date format should be adjusted. The withColumn operator should be used instead of the existing column assignment.

Which of the following describes a shuffle?

A. A shuffle is a process that is executed during a broadcast hash join.

B. A shuffle is a process that compares data across executors.

C. A shuffle is a process that compares data across partitions.

D. A shuffle is a Spark operation that results from DataFrame.coalesce().

E. A shuffle is a process that allocates partitions to executors.

Which of the following DataFrame methods is classified as a transformation?

A. DataFrame.count()

B. DataFrame.show()

C. DataFrame.select()

D. DataFrame.foreach()

E. DataFrame.first()

Which of the following code blocks performs an inner join between DataFrame itemsDf and DataFrame transactionsDf, using columns itemId and transactionId as join keys, respectively?

A. itemsDf.join(transactionsDf, "inner", itemsDf.itemId == transactionsDf.transactionId)

B. itemsDf.join(transactionsDf, itemId == transactionId)

C. itemsDf.join(transactionsDf, itemsDf.itemId == transactionsDf.transactionId, "inner")

D. itemsDf.join(transactionsDf, "itemsDf.itemId == transactionsDf.transactionId", "inner")

E. itemsDf.join(transactionsDf, col(itemsDf.itemId) == col(transactionsDf.transactionId))