Free Cloudera CCA175 Practice Exam with Questions & Answers

Problem Scenario 32 : You have given three files as below.

spark3/sparkdir1/file1.txt

spark3/sparkd ir2ffile2.txt

spark3/sparkd ir3Zfile3.txt

Each file contain some text.

spark3/sparkdir1/file1.txt

Apache Hadoop is an open-source software framework written in Java for distributed storage and distributed processing of very large data sets on computer clusters built from commodity hardware. All the modules in Hadoop are designed with a fundamental assumption that hardware failures are common and should be automatically handled by the framework

spark3/sparkdir2/file2.txt

The core of Apache Hadoop consists of a storage part known as Hadoop Distributed File System (HDFS) and a processing part called MapReduce. Hadoop splits files into large blocks and distributes them across nodes in a cluster. To process data, Hadoop transfers packaged code for nodes to process in parallel based on the data that needs to be processed.

spark3/sparkdir3/file3.txt

his approach takes advantage of data locality nodes manipulating the data they have access to to allow the dataset to be processed faster and more efficiently than it would be in a more conventional supercomputer architecture that relies on a parallel file system where computation and data are distributed via high-speed networking

Now write a Spark code in scala which will load all these three files from hdfs and do the word count by filtering following words. And result should be sorted by word count in reverse order.

Filter words ("a","the","an", "as", "a","with","this","these","is","are","in", "for", "to","and","The","of")

Also please make sure you load all three files as a Single RDD (All three files must be loaded using single API call).

You have also been given following codec

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

Please use above codec to compress file, while saving in hdfs.

Problem Scenario 64 : You have been given below code snippet.

val a = sc.parallelize(List("dog", "salmon", "salmon", "rat", "elephant"), 3)

val b = a.keyBy(_.length)

val c = sc.parallelize(Ust("dog","cat","gnu","salmon","rabbit","turkey","wolf","bear","bee"), 3)

val d = c.keyBy(_.length)

operation1

Write a correct code snippet for operationl which will produce desired output, shown below.

Array[(lnt, (Option[String], String))] = Array((6,(Some(salmon),salmon)), (6,(Some(salmon),rabbit}}, (6,(Some(salmon),turkey)), (6,(Some(salmon),salmon)), (6,(Some(salmon),rabbit)), (6,(Some(salmon),turkey)), (3,(Some(dog),dog)), (3,(Some(dog),cat)), (3,(Some(dog),gnu)), (3,(Some(dog),bee)), (3,(Some(rat), (3,(Some(rat),cat)), (3,(Some(rat),gnu)), (3,(Some(rat),bee)), (4,(None,wo!f)), (4,(None,bear)))

Problem Scenario 94 : You have to run your Spark application on yarn with each executor 20GB and number of executors should be 50. Please replace XXX, YYY, ZZZ

export HADOOP_CONF_DIR=XXX

./bin/spark-submit \

-class com.hadoopexam.MyTask \

xxx\

-deploy-mode cluster \ # can be client for client mode

YYY\

222 \

/path/to/hadoopexam.jar \

1000

Problem Scenario 92 : You have been given a spark scala application, which is bundled in jar named hadoopexam.jar.

Your application class name is com.hadoopexam.MyTask

You want that while submitting your application should launch a driver on one of the cluster node.

Please complete the following command to submit the application.

spark-submit XXX -master yarn \

YYY SSPARK HOME/lib/hadoopexam.jar 10

Problem Scenario 44 : You have been given 4 files , with the content as given below:

spark11/file1.txt

Apache Hadoop is an open-source software framework written in Java for distributed storage and distributed processing of very large data sets on computer clusters built from commodity hardware. All the modules in Hadoop are designed with a fundamental assumption that hardware failures are common and should be automatically handled by the framework

spark11/file2.txt

The core of Apache Hadoop consists of a storage part known as Hadoop Distributed File System (HDFS) and a processing part called MapReduce. Hadoop splits files into large blocks and distributes them across nodes in a cluster. To process data, Hadoop transfers packaged code for nodes to process in parallel based on the data that needs to be processed.

spark11/file3.txt

his approach takes advantage of data locality nodes manipulating the data they have access to to allow the dataset to be processed faster and more efficiently than it would be in a more conventional supercomputer architecture that relies on a parallel file system where computation and data are distributed via high-speed networking

spark11/file4.txt

Apache Storm is focused on stream processing or what some call complex event processing. Storm implements a fault tolerant method for performing a computation or pipelining multiple computations on an event as it flows into a system. One might use Storm to transform unstructured data as it flows into a system into a desired format

(spark11Afile1.txt)

(spark11/file2.txt)

(spark11/file3.txt)

(sparkl 1/file4.txt)

Write a Spark program, which will give you the highest occurring words in each file. With their file name and highest occurring words.

Problem Scenario 54 : You have been given below code snippet.

val a = sc.parallelize(List("dog", "tiger", "lion", "cat", "panther", "eagle"))

val b = a.map(x => (x.length, x))

operation1

Write a correct code snippet for operationl which will produce desired output, shown below.

Array[(lnt, String)] = Array((4,lion), (7,panther), (3,dogcat), (5,tigereagle))

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 63 : You have been given below code snippet.

val a = sc.parallelize(List("dog", "tiger", "lion", "cat", "panther", "eagle"), 2)

val b = a.map(x => (x.length, x))

operation1

Write a correct code snippet for operationl which will produce desired output, shown below. Array[(lnt, String}] = Array((4,lion), (3,dogcat), (7,panther), (5,tigereagle))

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.

Problem Scenario 1:

You have been given MySQL DB with following details.

user=retail_dba

password=cloudera

database=retail_db

table=retail_db.categories

jdbc URL = jdbc:mysql://quickstart:3306/retail_db

Please accomplish following activities.

1. Connect MySQL DB and check the content of the tables.

2. Copy "retaildb.categories" table to hdfs, without specifying directory name.

3. Copy "retaildb.categories" table to hdfs, in a directory name "categories_target".

4. Copy "retaildb.categories" table to hdfs, in a warehouse directory name "categories_warehouse".