Hadoop Interview Questions

In this blog you can find Hadoop interview questions and answers that have been asked in  reccent years in many companies.

1) What is  meant by Hadoop?

Hadoop is  distributed computing platform and  is written in Java. It consists of the following  features like Google File System and MapReduce.

2) mention which platform and Java version  required  to run Hadoop?

Java 1.6.x or advanced  versions are good for Hadoop work , preferably  Linux and Windows are the supported operating system for Hadoop environment , but BSD, Mac OS/X, and Solaris are more famous for working.

3) What are Hardware specifications for Hadoop?

Hadoop can run on  dual processor/ dual core machines along  with  4-8 GB RAM using ECC memory. It depends on the workflow designs 4.Describe most common input formats defined in Hadoop?The most common input formats  in Hadoop are:  
  1.  TextInputFormat
  2. KeyValueInputFormat
  3. SequenceFileInputFormat
TextInputFormat is  default input format.

5) How do you categorize a big data?

The big data is  categorized  by using the  features like:
  • Volume
  • Velocity
  • Variety

6) what is the use of .mecia class?

For the floating media objects from one side to another side , we use this class.

7) List the use of the bootstrap panel.

Panels in bootstrap from the boxing of DOM components.

8) Describe  purpose of button groups?

Button groups  used for  placement of more than one buttons in the same line. 9) List the various types of lists supported by Bootstrap.
  • Ordered list
  • Unordered list
  • Definition list

10) Name the command which  is used for the retrieval of the status of daemons running the Hadoop cluster?

The ‘jps’ command used for the retrieval of  status of daemons running the Hadoop cluster.

11) What is InputSplit ? Explain.

When a Hadoop job runs,  splits input files into chunks and assigns each split to mapper for processing. which is called the InputSplit.  

12) Explain textInputFormat?

TextInputFormat is  the text file  which is a record. Value is  content of the line while Key is the byte offset of the line. For example  Key: longWritable, Value: text

13) What is meant by  SequenceFileInputFormat in Hadoop?

SequenceFileInputFormat  in Hadoop is used to read files in sequence. It is the specific compressed binary file format which passes data between  output of one MapReduce job to the input of some another MapReduce job.

14) How many InputSplits can be  made by a Hadoop Framework?

Hadoop makes total 5 splits :
  • One split for 64K files
  • Two splits for 65MB files, and
  • Two splits for 127MB files

15) Describe the use of RecordReader in Hadoop?

InputSplit  assigned with  work but doesn’t know how to access . The record holder class is totally responsible for performing loading the data from its source and convert it to keys pair suitable for reading by  Mapper. The RecordReader’s instance can also be defined by the Input Format.

16)Describe JobTracker in Hadoop?

JobTracker is service within the Hadoop which runs MapReduce jobs on the cluster.

17) Explain WebDAV in Hadoop?

WebDAV is set of extension to HTTP which used to support editing and uploading the files. On most operating system WebDAV shares can  mounted as filesystems, so it is also possible to access HDFS as a standard filesystem by exposing HDFS over the WebDAV.

18) what is  Sqoop in Hadoop?

Sqoop is a tool which is used to transfer data between the Relational Database Management System and Hadoop HDFS. Using Sqoop, one  can transfer data from RDBMS like MySQL or Oracle into HDFS as well as exporting data from HDFS file to RDBMS.

19)List functionalities of JobTracker?

This  are the main tasks of JobTracker:
  •  accepting  jobs from the client.
  • communicating  with the NameNode to determine the location of the data.
  • To locate TaskTracker Nodes with free slots.
  • To submit  work to the chosen TaskTracker node and monitor  progress of each task.

20) Use of  TaskTracker.

TaskTracker is node in cluster that accepts jobs like MapReduce and Shuffle operations from  JobTracker.

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What is HADOOP?

Hadoop is open-source frame work for  storage and distributed processing of large datasets ,clusters of commodity hardware. It was developed by  Apache Software Foundation and is designed to handle jobs like holding vast amounts of data, including structured and unstructured data, across a distributed computing environment.

Hadoop is particularly well-suited for big data tasks like processing and  analytics checking.

The key responsibilities of a HADOOP DEVELOPER  will be as follows:

  • 1.Determine & Design Big Data solutions based on the requirements aligning with overall guiding principles of the project ecosystem
  • 2.Develop solutions as the  part of Scrum teams
  • 3.Build optimized data model, pipelines in the MPP environment
  • 4. Enhance  or build data engineering assets using Hadoop


  • 1.Knowledge of designing  principles and fundamentals of architecture
  • 2.Understanding  performanceof  engineering
  • 3.Knowledge on quality processes and estimation techniques
  • 4.Basic understanding of the project domain
  • 5.How to translate functional and  nonfunctional requirements of systems requirements
  • 6.How to design and perform code complex programs
  • 7.How  write test cases and scenarios based on specifications
  • 8.Good understanding of the SDLC and agile methodologies
  • 9.Awareness on  latest technologies and trends
  • 10.projects to improve Logical thinking and problem solving skills along with ability to collaborate
  • 11.Brief Knowledge on Hadoop Cluster architecture and  how to monitor cluster.
  • Is Hadoop good for Career?
    If you are a fresher and wondering what career to choose to grow your career in databases, then Hadoop is the best option to choose.



If you are a fresher or experienced job seeker  and  wondering which career option to choose to grow your career in databases, then Hadoop is the best option to choose.

Big Data provides various career paths like Data Analyst, Data Engineer,  Data Scientist, and Big Data Architect.

Choose any  specializatio based on your interests and career goals.

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Data Science coiurse is an emerging course offered by top  certification institutes. Data Science can be learned as  stand-alone program alos you can learn  under various specializations like Machine Learning, Python, Artificial Intelligence, Data Analytics, or Big Data.

Data science is a multi disciplinary course that uses various techniques, algorithms, processes, and systems to extract  insights and knowledge from structured and unstructured data.

Data science combines  of computer science, statistics, mathematics, domain knowledge, and data engineering to analyze the complex data sets and make informed decisions. 

1. Data Collection: Data scientists collect the data from various sources, including websites, databases, sensors, nd more. This data can be structured or unstructured 

2. Data Cleaning and Preprocessing:Raw data is  messy and incomplete. Data scientists clean , preprocess the data to  remove  any errors, outliers, and inconsistencies, making it suitable for the analysis.

3.Exploratory Data Analysis (EDA): EDA involves in  using statistical and visual techniques helps to  understand the data’s characteristics, like  distributions, correlations, and patterns. 

4. Feature Engineering: Feature engineering is  process of creating the new features or transforming the existing ones to improve the performance of the machine learning models. It involves selecting the most relevant variables and encoding  data.

5. Machine Learning:Machine learning is  subset of data science that always  focuse on using algorithms to build predictive models. which includes supervised learning , unsupervised learning , and reinforcement learning.

6. Model Training and Evaluation: Data scientists  also train machine learning models on historical data and evaluate  performance using various mesures. Cross-validation techniques are  most often used to assess model generalization

Data science is applied in real life in  various domains, including finance, marketing, healthcare, e-commerce, and more, to solve complex problems, make decisions, and gain a competitive advantage.

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Artificial Intelligence Interview Questions

LIST OF  Artificial Intelligence Interview Questions

1.LIST Common Uses and Applications of Artificial Intelligence?

Possibilities include  object detection,contract analysis and classification for  navigation and avoidance , image recognition, content- distribution, predictive maintenance, data processing, automation of manual tasks, and data driven reporting.

2. What are the Intelligent Agents, and How  They Used in AI?

Intelligent agents are the autonomous entities that uses sensor to know what is going on, and  use actuators to perform their tasks/ goals. They can be simple , complex and can be programmed to learn and accomplish their jobs better.

3. What is meant by Tensorflow, and What is Tensorflow  Used For?

Tensorflow is  open-source software library  was initially developed by  Google Brain Team to use in  neural networks research and machine learning . It is used for  the data-flow programming. TensorFlow makes  much easier to build the certain AI features into applications, including speech recognition and Natural language processing.

4. What is meant by  Machine Learning, and How can It Relate to AI?

Machine learning is  subset of AI. The idea is machines will “learn” and get better at  the tasks over  the time rather than having humans  having to input parameters. Machine learning is practical application of AI.

5. What are the Neural Networks, and How Does they Relate to AI?

Neural networks are the  class of machine learning algorithms. The neuron part of the neural is  computational component, and network part is how the neurons are being connected. Neural networks pass the data among themselves, gathering more and more meaning as the data moves in along. Because  networks are interconnected, more complex data can processed more efficiently.

6. What is Deep Learning, and How Does It Relate to AI?

Deep learning is subset of the machine learning. Refers to  using multi-layered neural networks to process the data in increasingly sophisticated ways, enabling  software to train itself to perform task like speech and image recognitions through the exposure to these vast amounts of data for continual improvement ability to recognize and process information. Layers of the neural networks stacked on the top of each for use in the  deep learning are called deep neural networks.

7. Why Image Recognition is a Key Function of AI?

Humans are visual, and AI  designed to emulate human brains. Teaching machines to recognize and categorize images is  crucial part of AI. Image recognition also helps the machines to learn  because the more images that are processed, are the better the software gets  recognizing and processing those images.

8. What is meant by Automatic Programming?

Automatic programming  describing what a program should do, and  having the AI system “write” the program.

9. What is a Bayesian Network, and How Does Bayesian  Relate to AI?

A Bayesian network is graphical model for the probabilistic relationships among a set of variables. It  alsomimics the human brain in processing variables.

10. What are the Constraint Satisfaction Problems?

Constraint Satisfaction Problems (CSPs) are the mathematical problems defined as  set of objects, the state  which must meet several constraints. CSPs are mostuseful for AI because the regularity of their formulation offers commonality for analyzing and solving the problems.

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