CCA175 Exam Questions & Answers

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CCA175 Exam Questions & Answers

Vendor: Cloudera

Certifications: Cloudera Certified Associate CCA

Exam Code: CCA175

Exam Name: CCA Spark and Hadoop Developer Exam

Updated: Dec 03, 2023

Q&As: 95

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CCA175 Online Practice Questions and Answers

Questions 1

Problem Scenario 18 : 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 Now accomplish following activities.


Create mysql table as below.

mysql --user=retail_dba -password=cloudera

use retail_db

CREATE TABLE IF NOT EXISTS departments_hive02(id int, department_name

varchar(45), avg_salary int);

show tables;


Now export data from hive table departments_hive01 in departments_hive02. While

exporting, please note following. wherever there is a empty string it should be loaded as a null value in


wherever there is -999 value for int field, it should be created as null value.

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Questions 2

Problem Scenario 68 : You have given a file as below. spark75/f ile1.txt File contain some text. As given Below spark75/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 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. 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 For a slightly more complicated task, lets look into splitting up sentences from our documents into word bigrams. A bigram is pair of successive tokens in some sequence. We will look at building bigrams from the sequences of words in each sentence, and then try to find the most frequently occuring ones. The first problem is that values in each partition of our initial RDD describe lines from the file rather than sentences. Sentences may be split over multiple lines. The glom() RDD method is used to create a single entry for each document containing the list of all lines, we can then join the lines up, then resplit them into sentences using "." as the separator, using flatMap so that every object in our RDD is now a sentence. A bigram is pair of successive tokens in some sequence. Please build bigrams from the sequences of words in each sentence, and then try to find the most frequently occuring ones.

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Questions 3

Problem Scenario 58 : You have been given below code snippet.

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



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

Array[(lnt, Seq[String])] = Array((4,ArrayBuffer(lion)), (6,ArrayBuffer(spider)),

(3,ArrayBuffer(dog, cat)), (5,ArrayBuffer(tiger, eagle}}}

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