Which of the following operations can be used to create a new DataFrame that has 12 partitions from an original DataFrame df that has 8 partitions?

Question

Options

• Adf.repartition(12)
• Bdf.cache()
• Cdf.partitionBy(1.5)
• Ddf.coalesce(12)
• Edf.partitionBy(12)

• A
  91% (39)
• C
  2% (1)
• D
  2% (1)
• E
  5% (2)

Explanation

repartition(n) performs a full shuffle of the data and can both increase and decrease the number of partitions to any target value. Since you need to go from 8 to 12 partitions (an increase), repartition(12) is the correct choice. coalesce(n), by contrast, can only reduce the number of partitions and avoids a full shuffle by merging existing partitions; calling coalesce(12) on an 8-partition DataFrame would have no effect since you cannot coalesce upward. partitionBy() is a write operation used with DataFrameWriter, not a transformation on a DataFrame object. df.cache() stores data in memory but does not change partition count.

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