Associate-Developer-Apache-Spark 試験問題を無料オンラインアクセス

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

The code block shown below should return a new 2-column DataFrame that shows one attribute from column attributes per row next to the associated itemName, for all suppliers in column supplier whose name includes Sports. Choose the answer that correctly fills the blanks in the code block to accomplish this.
Sample of DataFrame itemsDf:
1.+------+----------------------------------+-----------------------------+-------------------+
2.|itemId|itemName |attributes |supplier |
3.+------+----------------------------------+-----------------------------+-------------------+
4.|1 |Thick Coat for Walking in the Snow|[blue, winter, cozy] |Sports Company Inc.|
5.|2 |Elegant Outdoors Summer Dress |[red, summer, fresh, cooling]|YetiX |
6.|3 |Outdoors Backpack |[green, summer, travel] |Sports Company Inc.|
7.+------+----------------------------------+-----------------------------+-------------------+ Code block:
itemsDf.__1__(__2__).select(__3__, __4__)

• A.1. where
  2. "Sports".isin(col("Supplier"))
  3. "itemName"
  4. array_explode("attributes")
• B.1. filter
  2. col("supplier").isin("Sports")
  3. "itemName"
  4. explode(col("attributes"))
• C.1. filter
  2. col("supplier").contains("Sports")
  3. "itemName"
  4. explode("attributes")
• D.1. where
  2. col(supplier).contains("Sports")
  3. explode(attributes)
  4. itemName
• E.1. where
  2. col("supplier").contains("Sports")
  3. "itemName"
  4. "attributes"

The code block displayed below contains an error. The code block should combine data from DataFrames itemsDf and transactionsDf, showing all rows of DataFrame itemsDf that have a matching value in column itemId with a value in column transactionsId of DataFrame transactionsDf. Find the error.
Code block:
itemsDf.join(itemsDf.itemId==transactionsDf.transactionId)

• A.The union method should be used instead of join.
• B.The join method is inappropriate.
• C.The join expression is malformed.
• D.The merge method should be used instead of join.
• E.The join statement is incomplete.

Which of the following code blocks returns a copy of DataFrame transactionsDf that only includes columns transactionId, storeId, productId and f?
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.+-------------+---------+-----+-------+---------+----+

• A.transactionsDf.drop(value, predError)
• B.transactionsDf.drop(col("value"), col("predError"))
• C.transactionsDf.drop([col("predError"), col("value")])
• D.transactionsDf.drop(["predError", "value"])
• E.transactionsDf.drop("predError", "value")

The code block shown below should return a copy of DataFrame transactionsDf with an added column cos.
This column should have the values in column value converted to degrees and having the cosine of those converted values taken, rounded to two decimals. Choose the answer that correctly fills the blanks in the code block to accomplish this.
Code block:
transactionsDf.__1__(__2__, round(__3__(__4__(__5__)),2))

• A.1. withColumnRenamed
  2. "cos"
  3. cos
  4. degrees
  5. "transactionsDf.value"
• B.1. withColumn
  2. "cos"
  3. cos
  4. degrees
  5. transactionsDf.value
• C.1. withColumn
  2. col("cos")
  3. cos
  4. degrees
  5. transactionsDf.value
• D.1. withColumn
  2. col("cos")
  3. cos
  4. degrees
  5. col("value")
  E
  . 1. withColumn
  2. "cos"
  3. degrees
  4. cos
  5. col("value")