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Exam Name: CCA Spark and Hadoop Developer Exam
Certification Name: CCA Spark and Hadoop Developer

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Problem Scenario 65 : You have been given below code snippet.
val a = sc.parallelize(List("dog", "cat", "owl", "gnu", "ant"), 2)
val b = sc.parallelize(1 to a.count.tolnt, 2)
val c = a.zip(b)
operation1
Write a correct code snippet for operationl which will produce desired output, shown below.
Array[(String, Int)] = Array((owl,3), (gnu,4), (dog,1), (cat,2>, (ant,5))

Answer: See the explanation for Step by Step Solution and configuration.
Explanation:
Solution : c.sortByKey(false).collect
sortByKey [Ordered] : This function sorts the input RDD's data and stores it in a new RDD.
"The output RDD is a shuffled RDD because it stores data that is output by a reducer
which has been shuffled. The implementation of this function is actually very clever.
First, it uses a range partitioner to partition the data in ranges within the shuffled RDD.
Then it sorts these ranges individually with mapPartitions using standard sort mechanisms.





Problem Scenario 72 : You have been given a table named "employee2" with following
detail.
first_name string
last_name string
Write a spark script in python which read this table and print all the rows and individual
column values.

Answer: See the explanation for Step by Step Solution and configuration.
Explanation:
Solution :
Step 1 : Import statements for HiveContext from pyspark.sql import HiveContext
Step 2 : Create sqIContext sqIContext = HiveContext(sc)
Step 3 : Query hive
employee2 = sqlContext.sql("select' from employee2")
Step 4 : Now prints the data for row in employee2.collect(): print(row)
Step 5 : Print specific column for row in employee2.collect(): print( row.fi rst_name)





Problem Scenario 67 : You have been given below code snippet.
lines = sc.parallelize(['lts fun to have fun,','but you have to know how.'])M = lines.map( lambda x: x.replace(',7 ').replace('.',' 'J.replaceC-V ').lower())
r2 = r1.flatMap(lambda x: x.split())
r3 = r2.map(lambda x: (x, 1))
operation1
r5 = r4.map(lambda x:(x[1],x[0]))
r6 = r5.sortByKey(ascending=False)
r6.take(20)
Write a correct code snippet for operationl which will produce desired output, shown below.
[(2, 'fun'), (2, 'to'), (2, 'have'), (1, its'), (1, 'know1), (1, 'how1), (1, 'you'), (1, 'but')]

Answer: See the explanation for Step by Step Solution and configuration.
Explanation:
Solution :
r4 = r3.reduceByKey(lambda x,y:x+y)



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