Basics Of Hadoop(Engineering > Computer Science And Engineering > Hadoop ) Questions and Answers

Question 1. ___________ is general-purpose computing model and runtime system for distributed data analytics.
  1.    Mapreduce
  2.    Drill
  3.    Oozie
  4.    None of the mentioned
Explanation:-
Answer: Option A. -> Mapreduce


Mapreduce provides a flexible and scalable foundation for analytics, from traditional reporting to leading-edge machine learning algorithms.



Question 2. What was Hadoop written in ?
  1.    Java (software platform)
  2.    Perl
  3.    Java (programming language)
  4.    Lua (programming language)
Explanation:-
Answer: Option C. -> Java (programming language)


The Hadoop framework itself is mostly written in the Java programming language, with some native code in C and command line utilities written as shell-scripts.



Question 3. Which of the following platforms does Hadoop run on ?
  1.    Bare metal
  2.    Debian
  3.    Cross-platform
  4.    Unix-like
Explanation:-
Answer: Option C. -> Cross-platform


Hadoop has support for cross platform operating system.



Question 4. Which of the following genres does Hadoop produce ?
  1.    Distributed file system
  2.    JAX-RS
  3.    Java Message Service
  4.    Relational Database Management System
Explanation:-
Answer: Option A. -> Distributed file system


The Hadoop Distributed File System (HDFS) is designed to store very large data sets reliably, and to stream those data sets at high bandwidth to user.



Question 5. Hadoop achieves reliability by replicating the data across multiple hosts, and hence does not require ________ storage on hosts.
  1.    RAID
  2.    Standard RAID levels
  3.    ZFS
  4.    Operating system
Explanation:-
Answer: Option A. -> RAID


With the default replication value, 3, data is stored on three nodes: two on the same rack, and one on a different rack.



Question 6. Point out the wrong statement :
  1.    Hardtop's processing capabilities are huge and its real advantage lies in the ability to process terabytes & petabytes of data
  2.    Hadoop uses a programming model called MapReduce", all the programs should confirms to this model in order to work on Hadoop platform
  3.    The programming model, MapReduce, used by Hadoop is difficult to write and test
  4.    All of the mentioned
Explanation:-
Answer: Option C. -> The programming model, MapReduce, used by Hadoop is difficult to write and test


The programming model, MapReduce, used by Hadoop is simple to write and test.



Question 7. All of the following accurately describe Hadoop, EXCEPT:
  1.    Open source
  2.    Real-time
  3.    Java-based
  4.    Distributed computing approach
Explanation:-
Answer: Option B. -> Real-time


Apache Hadoop is an open-source software framework for distributed storage and distributed processing of Big Data on clusters of commodity hardware.



Question 8. What was Hadoop named after?
  1.    Creator Doug Cutting's favorite circus act
  2.    Cutting's high school rock band
  3.    The toy elephant of Cutting's son
  4.    A sound Cutting's laptop made during Hadoop's development
Explanation:-
Answer: Option C. -> The toy elephant of Cutting's son


Doug Cutting, Hadoop's creator, named the framework after his child's stuffed toy elephant.



Question 9. Point out the wrong statement :
  1.    Elastic MapReduce (EMR) is Facebook's packaged Hadoop offering
  2.    Amazon Web Service Elastic MapReduce (EMR) is Amazon's packaged Hadoop offering
  3.    Scalding is a Scala API on top of Cascading that removes most Java boilerplate
  4.    All of the mentioned
Explanation:-
Answer: Option A. -> Elastic MapReduce (EMR) is Facebook's packaged Hadoop offering


Rather than building Hadoop deployments manually on EC2 (Elastic Compute Cloud) clusters, users can spin up fully configured Hadoop installations using simple invocation commands, either through the AWS Web Console or through command-line tools.



Question 10. ________ is the most popular high-level Java API in Hadoop Ecosystem
  1.    Scalding
  2.    HCatalog
  3.    Cascalog
  4.    Cascading
Explanation:-
Answer: Option D. -> Cascading


Cascading hides many of the complexities of MapReduce programming behind more intuitive pipes and data flow abstractions.