Apache Spark Spark Core and RDDs 3

Question 1

What is the purpose of the reduceByKey() transformation in Spark?

Accepted Answer

Aggregates values for each key using a given function

Answer

Reduces the number of keys in an RDD

Answer

Removes duplicate keys from an RDD

Answer

Sorts keys in descending order

Question 2

Which of these creates an RDD from an existing Scala/Python collection in Spark?

Accepted Answer

sc.parallelize()

Answer

sc.textFile()

Answer

sc.createRDD()

Answer

sc.loadData()

Question 3

What is the role of the SparkContext in a Spark application?

Accepted Answer

Serves as the entry point for Spark functionality and represents the connection to a Spark cluster

Answer

Manages SQL queries

Answer

Schedules streaming jobs

Answer

Provides the DataFrame API

Question 4

What does the coalesce() transformation do in Spark?

Accepted Answer

Reduces the number of partitions with minimal data shuffling

Answer

Increases the number of partitions

Answer

Merges two RDDs into one

Answer

Caches the RDD to memory

Question 5

Which action computes the sum of all elements in a numeric RDD?

Accepted Answer

reduce(lambda a,b: a+b)

Answer

sum()

Answer

aggregate()

Answer

fold()

Question 6

What does Spark's union() transformation return?

Accepted Answer

An RDD containing all elements from both RDDs, including duplicates

Answer

An RDD containing only elements common to both RDDs

Answer

An RDD containing elements in the first but not the second RDD

Answer

An RDD with duplicate elements removed