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Development Development Tools Hadoop

Apache Hadoop and Mapreduce Interview Questions and Answers

Apache Hadoop and Mapreduce Interview Questions and Answers (120+ FAQ)
Rating: 4.0 out of 54.0 (1 rating)
77 students
Created by Bigdata Engineer
Last updated 10/2020
English
30-Day Money-Back Guarantee

What you'll learn

  • By attending this course you will get to know frequently and most likely asked Programming, Scenario based, Fundamentals, and Performance Tuning based Question asked in Apache Hadoop and Mapreduce Interview along with the answer
  • This will help Bigdata Career Aspirants to prepare for the interview.
  • During your Scheduled Interview you do not have to spend time searching the Internet for Apache Hadoop and Mapreduce Interview questions.
  • We have already compiled the most frequently asked and latest Apache Hadoop and Mapreduce Interview questions in this course.

Course content

13 sections • 129 lectures • 1h 48m total length

  • Preview01:55
  • Preview00:36
  • Preview03:40
  • How does Hadoop Namenode failover process works?
    04:16
  • Scenario Based Question
    00:43
  • How can we initiate a manual failover when automatic failover is configured?
    00:25
  • When not use Hadoop?
    02:26
  • Is there a simple command for hadoop that can change the name of a file ?
    00:32
  • When To Use Hadoop?
    02:27
  • Scenario Based Question
    00:46

  • Can I have multiple files in HDFS use different block sizes?
    00:15
  • Scenario Based Question
    01:17
  • As we talk about Hadoop is Highly scalable how well does it Scale?
    00:24
  • What platforms and Java versions does Hadoop run on?
    00:29
  • What kind of hardware scales best for Hadoop?
    00:41
  • Is there an easy way to see the status and health of a cluster?
    00:27
  • Scenario Based Question
    01:59
  • Scenario Based Question
    01:34
  • Scenario Based Question
    03:47
  • Scenario Based Question
    00:45

  • Preview04:32
  • Does Hadoop require SSH?
    00:39
  • What does NFS: Cannot create lock on (some dir) mean?
    01:38
  • Scenario Based Question
    00:58
  • Preview02:02
  • Scenario Based Question
    00:44
  • Scenario Based Question
    00:21
  • Scenario Based Question
    01:53
  • Scenario Based Question
    01:17
  • Scenario Based Question
    00:53

  • What is the purpose of the secondary name-node?
    00:48
  • Scenario Based Question
    01:10
  • How do I set up a hadoop node to use multiple volumes?
    01:22
  • Scenario Based Question
    01:09
  • Does HDFS make block boundaries between records?
    00:16
  • Does Wildcard characters work correctly in FsShell?
    00:27
  • What does "file could only be replicated to 0 nodes, instead of 1" mean?
    00:29
  • Scenario Based Question
    01:05
  • What happens when two clients try to write into the same HDFS file?
    00:39
  • How to limit Data node's disk usage?
    00:04

  • Scenario Based Question
    00:30
  • Scenario Based Question
    01:03
  • On an individual data node, how do you balance the blocks on the disk?
    00:35
  • Scenario Based Question
    02:30
  • Difference between hadoop fs -put and hadoop fs -copyFromLocal?
    00:54
  • Scenario Based Question
    00:51
  • How to check HDFS Directory size?
    00:10
  • Scenario Based Question
    00:14
  • On what concept the Hadoop framework works?
    01:18
  • What is Hadoop streaming?
    00:34

  • Explain about the process of inter cluster data copying.?
    00:35
  • Scenario Based Question
    01:11
  • Differentiate between Structured and Unstructured data?
    00:57
  • Explain the difference between NameNode, Backup Node and Checkpoint NameNode?
    00:44
  • How can you overwrite the replication factors in HDFS?
    01:18
  • What is the process to change the files at arbitrary locations in HDFS?
    00:29
  • Explain about the indexing process in HDFS?
    00:20
  • What is a rack awareness and on what basis is data stored in a rack?
    01:11
  • What happens to a NameNode that has no data?
    00:17
  • Scenario Based Question
    00:19

  • Scenario Based Question
    00:15
  • Whenever a client submits a hadoop job, who receives it?
    00:23
  • What do you understand by edge nodes in Hadoop?
    00:26
  • What are real-time industry applications of Hadoop?
    00:45
  • What all modes Hadoop can be run in?
    00:35
  • Explain the major difference between HDFS block and InputSplit?
    00:49
  • What are the most common Input Formats in Hadoop?
    00:25
  • What is Speculative Execution in Hadoop?
    00:39
  • What is Fault Tolerance?
    00:37
  • What is a heartbeat in HDFS?
    00:42

  • How to keep HDFS cluster balanced?
    01:51
  • How to deal with small files in Hadoop?
    00:38
  • Scenario Based Question
    00:42
  • What type of problems can mapreduce solve?
    00:35
  • What is the difference between Hadoop Map Reduce and Google Map Reduce?
    00:22
  • How to get the input file name in the mapper in a Hadoop program?
    00:21
  • Scenario Based Question
    00:35
  • Scenario Based Question
    01:10
  • Scenario Based Question
    00:45
  • Can you set number of map task in Map reduce?
    00:26

  • If your Mapreduce Job launches 20 task for 1 job can you limit to 10 task?
    00:25
  • Scenario Based Question
    00:35
  • What is Shuffling and Sorting in Hadoop MapReduce?
    01:26
  • How do I submit extra content (jars, static files, etc) for Mapreduce job to use
    00:47
  • How do I get my MapReduce Java Program to read the Cluster's set configuration?
    00:44
  • Explain what happens when Hadoop spawned 50 tasks for a job and one of the task
    00:20
  • What is OutputCommitter?
    00:56
  • What is RecordReader in a Map Reduce?
    00:20
  • What is a MapReduce Combiner?
    00:22
  • What do you understand by the term Straggler ?
    00:13

  • What is identity Mapper and identity reducer?
    00:37
  • What is the role of a MapReduce partitioner?
    00:24
  • When should you use a reducer?
    00:20
  • What steps do you follow in order to improve the performace of Mapreduce Job?
    00:38
  • What is the purpose of shuffling and sorting phase in the reducer in Map Reduce
    01:03
  • Scenario Based Question
    00:29
  • What do you understand by compute and storage nodes?
    00:18
  • Is it possible to rename the output file?
    00:10
  • What is the default input type in MapReduce?
    00:08
  • How is reporting controlled in hadoop?
    00:11

Requirements

  • Apache Hadoop and Mapreduce basic fundamental knowledge is required

Description

Apache Hadoop and Mapreduce Interview Questions has a collection of 120+ questions with answers asked in the interview for freshers and experienced (Programming, Scenario-Based, Fundamentals, Performance Tuning based Question and Answer).

This  course is intended to help Apache Hadoop and Mapreduce Career Aspirants to prepare for the interview.

We are planning to add more questions in upcoming versions of this course.


The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Rather than rely on hardware to deliver high-availability, the library itself is designed to detect and handle failures at the application layer, so delivering a highly-available service on top of a cluster of computers, each of which may be prone to failures.


Hadoop MapReduce is a software framework for easily writing applications which process vast amounts of data (multi-terabyte data-sets) in-parallel on large clusters (thousands of nodes) of commodity hardware in a reliable, fault-tolerant manner.

A MapReduce job usually splits the input data-set into independent chunks which are processed by the map tasks in a completely parallel manner. The framework sorts the outputs of the maps, which are then input to the reduce tasks. Typically both the input and the output of the job are stored in a file-system. The framework takes care of scheduling tasks, monitoring them and re-executes the failed tasks.

Typically the compute nodes and the storage nodes are the same, that is, the MapReduce framework and the Hadoop Distributed File System (see HDFS Architecture Guide) are running on the same set of nodes. This configuration allows the framework to effectively schedule tasks on the nodes where data is already present, resulting in very high aggregate bandwidth across the cluster.


Course Consist of the Interview Question on the following Topics

  • Single Node Setup

  • Cluster Setup

  • Commands Reference

  • FileSystem Shell

  • Compatibility Specification

  • Interface Classification

  • FileSystem Specification

  • Common

  • CLI Mini Cluster

  • Native Libraries

  • HDFS

  • Architecture

  • Commands Reference

  • NameNode HA With QJM

  • NameNode HA With NFS

  • Federation

  • ViewFs

  • Snapshots

  • Edits Viewer

  • Image Viewer

  • Permissions and HDFS

  • Quotas and HDFS

  • Disk Balancer

  • Upgrade Domain

  • DataNode Admin

  • Router Federation

  • Provided Storage

  • MapReduce

  • Distributed Cache Deploy

  • Support for YARN Shared Cache

  • MapReduce REST APIs

  • MR Application Master

  • MR History Server

  • YARN

  • Architecture

  • Commands Reference

  • ResourceManager Restart

  • ResourceManager HA

  • Node Labels

  • Node Attributes

  • Web Application Proxy

  • Timeline Server

  • Timeline Service V.2

  • Writing YARN Applications

  • YARN Application Security

  • NodeManager

  • Using CGroups

  • YARN Federation

  • Shared Cache

  • YARN UI2

  • YARN REST APIs

  • Introduction

  • Resource Manager

  • Node Manager

  • Timeline Server

  • Timeline Service V.2

  • YARN Service

  • Yarn Service API

  • Hadoop Streaming

  • Hadoop Archives

  • Hadoop Archive Logs

  • DistCp

  • Hadoop Benchmarking

  • Reference

  • Changelog and Release Notes

  • Configuration

  • core-default.xml

  • hdfs-default.xml

  • hdfs-rbf-default.xml

  • mapred-default.xml

  • yarn-default.xml

  • Deprecated Properties

Who this course is for:

  • This course is designed for Apache Hadoop and Mapreduce Job seeker with 6 months to 2 years of Experience in Apache Hadoop and Mapreduce or Big data Hadoop Development and looking out for new job as Developer,Bigdata Engineers or Developers, Software Developer, Software Architect, Development Manager

Instructor

Bigdata Engineer
Bigdata Engineer
Bigdata Engineer
  • 3.4 Instructor Rating
  • 264 Reviews
  • 15,305 Students
  • 20 Courses

I am Solution Architect with 12+ year’s of experience in Banking, Telecommunication and Financial Services industry across a diverse range of roles in Credit Card, Payments, Data Warehouse and Data Center programmes

My role as Bigdata and Cloud Architect to work as part of Bigdata team to provide Software Solution.

Responsibilities includes,

- Support all Hadoop related issues
- Benchmark existing systems, Analyse existing system challenges/bottlenecks and Propose right solutions to eliminate them based on various Big Data technologies
- Analyse and Define pros and cons of various technologies and platforms
- Define use cases, solutions and recommendations
- Define Big Data strategy
- Perform detailed analysis of business problems and technical environments
- Define pragmatic Big Data solution based on customer requirements analysis
- Define pragmatic Big Data Cluster recommendations
- Educate customers on various Big Data technologies to help them understand pros and cons of Big Data
- Data Governance
- Build Tools to improve developer productivity and implement standard practices

I am sure the knowledge in these courses can give you extra power to win in life.

All the best!!

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