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CCA Spark and Hadoop Developer Exam Practice Questions
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CCA175 Cloudera Exam Dumps
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CCA175 FAQs
The Cloudera CCA175 exam, also known as the Cloudera Certified Associate Spark and Hadoop Developer exam, tests a candidate's proficiency in using Apache Hadoop and Apache Spark for big data processing. It is a hands-on, practical exam where candidates complete tasks in a live environment?.
With a Cloudera CCA175 certification, you can pursue roles such as Big Data Developer, Hadoop Developer, Spark Developer, Data Engineer, and Data Analyst. These roles involve designing, developing, and managing big data applications using Hadoop and Spark?.
Certified Spark and Hadoop developers typically earn between $90,000 and $130,000 annually, depending on experience, location, and specific job roles. This certification can significantly enhance career prospects and earning potential in the field of big data?.
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CCA Spark and Hadoop Developer Exam Questions and Answers
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.
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