Cloudera CCA175 Dumps

Problem Scenario 34 : You have given a file named spark6/user.csv.

Data is given below:

user.csv

id,topic,hits

Rahul,scala,120

Nikita,spark,80

Mithun,spark,1

myself,cca175,180

Now write a Spark code in scala which will remove the header part and create RDD of values as below, for all rows. And also if id is myself" than filter out row.

Map(id -> om, topic -> scala, hits -> 120)

Problem Scenario 48 : You have been given below Python code snippet, with intermediate output.

We want to take a list of records about people and then we want to sum up their ages and count them.

So for this example the type in the RDD will be a Dictionary in the format of {name: NAME, age:AGE, gender:GENDER}.

The result type will be a tuple that looks like so (Sum of Ages, Count)

people = []

people.append({'name':'Amit', 'age':45,'gender':'M'})

people.append({'name':'Ganga', 'age':43,'gender':'F'})

people.append({'name':'John', 'age':28,'gender':'M'})

people.append({'name':'Lolita', 'age':33,'gender':'F'})

people.append({'name':'Dont Know', 'age':18,'gender':'T'})

peopleRdd=sc.parallelize(people) //Create an RDD

peopleRdd.aggregate((0,0), seqOp, combOp) //Output of above line : 167, 5)

Now define two operation seqOp and combOp , such that

seqOp : Sum the age of all people as well count them, in each partition. combOp : Combine results from all partitions.

Problem Scenario 38 : You have been given an RDD as below,

val rdd: RDD[Array[Byte]]

Now you have to save this RDD as a SequenceFile. And below is the code snippet.

import org.apache.hadoop.io.compress.GzipCodec

rdd.map(bytesArray => (A.get(), new B(bytesArray))).saveAsSequenceFile('7output/path",classOt[GzipCodec])

What would be the correct replacement for A and B in above snippet.