Which of the following DataFrame operators is never classified as a wide transformation?

• A. DataFrame.select()
• B. DataFrame.join()
• C. DataFrame.aggregate()
• D. DataFrame.sort()
• E. DataFrame.repartition()

Which of the following code blocks returns a one-column DataFrame for which every row contains an array of all integer numbers from 0 up to and including the number given in column predError of DataFrame transactionsDf, and null if predError is null?
Sample of DataFrame transactionsDf:
1.+-------------+---------+-----+-------+---------+----+
2.|transactionId|predError|value|storeId|productId| f|
3.+-------------+---------+-----+-------+---------+----+
4.| 1| 3| 4| 25| 1|null|
5.| 2| 6| 7| 2| 2|null|
6.| 3| 3| null| 25| 3|null|
7.| 4| null| null| 3| 2|null|
8.| 5| null| null| null| 2|null|
9.| 6| 3| 2| 25| 2|null|
10.+-------------+---------+-----+-------+---------+----+

• A. 1.def count_to_target(target):
  2. if target is None:
  3. return
  4.
  5. result = list(range(target))
  6. return result
  7.
  8.count_to_target_udf = udf(count_to_target)
  9.
  10.transactionsDf.select(count_to_target_udf('predError'))
• B. 1.def count_to_target(target):
  2. result = list(range(target))
  3. return result
  4.
  5.count_to_target_udf = udf(count_to_target, ArrayType(IntegerType()))
  6.
  7.df = transactionsDf.select(count_to_target_udf('predError'))
• C. 1.def count_to_target(target):
  2. if target is None:
  3. return
  4.
  5. result = [range(target)]
  6. return result
  7.
  8.count_to_target_udf = udf(count_to_target, ArrayType[IntegerType])
  9.
  10.transactionsDf.select(count_to_target_udf(col('predError')))
• D. 1.def count_to_target(target):
  2. if target is None:
  3. return
  4.
  5. result = list(range(target))
  6. return result
  7.
  8.transactionsDf.select(count_to_target(col('predError')))
• E. 1.def count_to_target(target):
  2. if target is None:
  3. return
  4.
  5. result = list(range(target))
  6. return result
  7.
  8.count_to_target_udf = udf(count_to_target, ArrayType(IntegerType()))
  9.
  10.transactionsDf.select(count_to_target_udf('predError'))
  (Correct)

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_split")A.

• A. Partitioning data by storeId is possible with the bucketBy expression, so partitionOn should be replaced by bucketBy.
• B. Partitioning data by storeId is possible with the partitionBy expression, so partitionOn should be replaced by partitionBy.
• C. The format("parquet") expression should be removed and instead, the information should be added to the write expression like so: write("parquet").
• D. 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.
• E. partitionOn("storeId") should be called before the write operation.

Which of the following code blocks performs an inner join of DataFrames transactionsDf and itemsDf on columns productId and itemId, respectively, excluding columns value and storeId from DataFrame transactionsDf and column attributes from DataFrame itemsDf?

• A. transactionsDf.drop("value", "storeId").join(itemsDf.drop("attributes"),
  "transactionsDf.productId==itemsDf.itemId")
• B. 1.transactionsDf \
  2. .drop(col('value'), col('storeId')) \
  3. .join(itemsDf.drop(col('attributes')), col('productId')==col('itemId'))
• C. 1.transactionsDf.createOrReplaceTempView('transactionsDf')
  2.itemsDf.createOrReplaceTempView('itemsDf')
  3.
  4.statement = """
  5.SELECT * FROM transactionsDf
  6.INNER JOIN itemsDf
  7.ON transactionsDf.productId==itemsDf.itemId
  8."""
  9.spark.sql(statement).drop("value", "storeId", "attributes")
• D. transactionsDf.drop('value', 'storeId').join(itemsDf.select('attributes'), transactionsDf.productId==itemsDf.itemId)
• E. 1.transactionsDf.createOrReplaceTempView('transactionsDf')
  2.itemsDf.createOrReplaceTempView('itemsDf')
  3.
  4.spark.sql("SELECT -value, -storeId FROM transactionsDf INNER JOIN itemsDf ON productId==itemId").drop("attributes")