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Cloudera CCA175 Exam Overview:

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Cloudera CCA175 Exam Questions Answered

Cloudera CCA Spark and Hadoop Developer Sample Questions:

Question 1

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Problem Scenario 11 : You have been given following mysql database details as well as other info.
user=retail_dba
password=cloudera
database=retail_db
jdbc URL = jdbc:mysql://quickstart:3306/retail_db
Please accomplish following.
1. Import departments table in a directory called departments.
2. Once import is done, please insert following 5 records in departments mysql table.
Insert into departments(10, physics);
Insert into departments(11, Chemistry);
Insert into departments(12, Maths);
Insert into departments(13, Science);
Insert into departments(14, Engineering);
3. Now import only new inserted records and append to existring directory . which has been created in first step.

Question 2

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Problem Scenario 17 : You have been given following mysql database details as well as other info.
user=retail_dba
password=cloudera
database=retail_db
jdbc URL = jdbc:mysql://quickstart:3306/retail_db
Please accomplish below assignment.
1. Create a table in hive as below, create table departments_hiveOl(department_id int, department_name string, avg_salary int);
2. Create another table in mysql using below statement CREATE TABLE IF NOT EXISTS departments_hive01(id int, department_name varchar(45), avg_salary int);
3. Copy all the data from departments table to departments_hive01 using insert into departments_hive01 select a.*, null from departments a;
Also insert following records as below
insert into departments_hive01 values(777, "Not known",1000);
insert into departments_hive01 values(8888, null,1000);
insert into departments_hive01 values(666, null,1100);
4. Now import data from mysql table departments_hive01 to this hive table. Please make sure that data should be visible using below hive command. Also, while importing if null value found for department_name column replace it with "" (empty string) and for id column with -999 select * from departments_hive;

Question 3

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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))

Question 4

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Problem Scenario 33 : You have given a files as below.
spark5/EmployeeName.csv (id,name)
spark5/EmployeeSalary.csv (id,salary)
Data is given below:
EmployeeName.csv
E01,Lokesh
E02,Bhupesh
E03,Amit
E04,Ratan
E05,Dinesh
E06,Pavan
E07,Tejas
E08,Sheela
E09,Kumar
E10,Venkat
EmployeeSalary.csv
E01,50000
E02,50000
E03,45000
E04,45000
E05,50000
E06,45000
E07,50000
E08,10000
E09,10000
E10,10000
Now write a Spark code in scala which will load these two tiles from hdfs and join the same, and produce the (name.salary) values.
And save the data in multiple tile group by salary (Means each file will have name of employees with same salary). Make sure file name include salary as well.

Question 5

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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.

Solutions: