Thursday, 29 October 2020

DOCKER INTERVIEW QUESTIONS AND ANSWERS – WHAT IS DOCKER

 


Docker Interview Questions And Answers: Explained

Docker, which was kicked off in 2013, got a boom in the IT industry by the end of 2017. In 2017, there was an exponential rise in the downloading of container image rates. With an increase in demand, it increased its recruitments subsequently. The employment rate got a strike, and the new job openings brought scopes for a better career. To crack the job and work with Docker, you need to pass the interview. And to pass the Docker interview questions and answers is definitely not a game of words. To make you achieve your goal with ease, we have listed down a few important Docker Interview questions and answers. So, here are the top interview questions that will help you out.

The Docker interview questions consist of a good rational question pattern. Starting from basics to advanced questions, the interviewer will test your ability and caliber to pass this by gradually advancing the level of the Docker Interview Questions. Usually, the interviewer goes from the basic level of the questions and then move towards the advanced one. Here are the Docker Interview Questions –

 Docker Interview Questions and answers– let's get started

 

 Docker Interview Questions

  • What is Docker?

Answers. It is a tool of intermodal freight transfer port that uses ISO containers to pack applications and the dependencies together. It packs them in the form of containers and also ensures that your application is working effectively. The efficiency remains seamless in production or development or test or environment. The container covers software in a file system consisting of code, system tools, personalization, system library, run time, and other essentials that are required to run them. Along with that, it stores the capacity to wrap anything installed on the server.

 
Docker Interview Questions-  Part 1
  • List some important features of Docker.

 

Answers:

  • It has amazing operational efficiency.
  • The best part is its easy modeling.
  • It has good developer productivity 
  • Affinity rate 

 

  • What is Docker Container?

Answers. One of the critical questions related to Docker. It’s an ISO container that is used to pack the applications with the dependency. Container covers software in a file system that contains tools, code, etc. this Container can work in any computer or environment, in any infrastructure, in any cloud. It shares computing with other containers. Then it runs under an isolated process. This is done on the host OS in the userspace. It is formed by units of images.

 

  • What are the states in which a Docker container is found to be?

Answers;

  1. Running
  2. Paused
  3. Restarting 
  4. Existed

 

  • What is Docker image? 

 

Answers. The image is the building unit or source of the Docker container. These images can be brought into play in any Docker environment.

 

  • What is Docker hub?

 

The images compile to create a Docker container. The registry where images live is called a hub. It is one of the largest public storehouses of image containers. Users are able to pick images and create their own containers and customized images.

 

  • What do you mean by Hypervisor? 

 

Answers:  That is a regular one in Docker Interview questions when it comes to the Docker Interview. The hypervisor is software that enables Virtualization, and hence, it is also known as Virtual Machine Monitor. It allows the host system and then assigns its resources as required to each virtual segment. On a single host system, we can have multiple Operating Systems. 

Further, we have two kinds of Hypervisor 

 

  1.  Native Hypervisor 

 

  1. Hosted Hypervisor 

 

Native Hypervisor: This Hypervisor is also known as a Bare-metal Hypervisor. It works directly on the basal host system. So, it doesn’t need a base server OS.

Hosted Hypervisor: This Hypervisor computes the underlying host system. So, it is known as Hosted Hypervisor.

 

  • What do you mean by Virtualization?

 

Answers: This Question has great potential in the Docker Interview. The process of developing a software-based virtual environment such as servers, applications, etc. is called Virtualization. In Virtualization, these virtual versions are formed of one physical hardware system. By using a software called Hypervisor, one system is splitter into many different sections. After splitting, each section works distinctly. This is done through Virtualization. The virtual version formed using hypervisor is known as a Virtual Machine. 

 

  • What do you mean by containerization?

 

Answers: While developing software on one machine, there are possibilities of malfunction of the code developed in one computer. The code might not work properly, and then, to solve this problem, the containerization concept is used. An application that is developed and stuffed into a bundle with its configuration files and dependencies into a container. The container enabled will form a bug-free environment with any configurations and libraries bundled together. It is one of the best and famous containerization environment.

 

Docker Interview Questions can also include differences and comparisons. You need to answers \each of the questions wisely to shine in front of the interviewers.

 

  • List the differences between Virtualization and containerization.

Answers: This can be an assured question in your interview. You need to answers on point and impress the interviewers with your knowledge.

Containers give an environment to run the application where the complete user space is extremely dedicated to the application. Every container functions distinctly. Even if changes are made in the container, it will never have an impact on the host or other distinctive working containers running under the same host. 

While, during Virtualization, a software called Hypervisor provides a virtual machine entirely to the guest

Each virtual machine is a physical machine, while each container is a different application.

Containers are extraction of the application layer, while virtual machines are extraction of the hardware layer.

 

  • What is  Docker Architecture?

 

Answers. Docker Architecture has Docker Engine. It is a client-server application. This is one of the major questions that is regularly asked in the Interviews.

 

The major components of the Docker Engine are : 

  • The server that is a long-running program called a daemon process
  • REST API with coded programs that are used to interact with the daemon and instruct it what and when to do
  • CLI – Command Line Interface client
  • The Command Line Interface (CLI) uses Docker REST API to control and talk to Docker daemon by scripting, or direct Command Line Interface (CLI) commands. Many more Docker applications use REST API and CLI direct command.

 

  • What does CNM stand for?

Answers. CNM is the abbreviation for Container Networking Model. It is the basis of container networking in the Docker version. You can answers this kind of Questions, in a small a contained sentence.

 

  • What is Docker file?

Answers. It is known as the file that provides an introduction to Docker to form images by reading that information. This file is basically said to be a text document. It consists of all commands that a user would use on the command line to form an image. Users can create an automated build known as Docker build. This can execute different command-line instructions.

  • What do you mean by Docker compose?

 

Answers. A -compose is a YAML file. It consists of crude details about services, networks, etc. This is used to set up the applications. By using this compose, one can create a distinct container and host them to get them so that they can communicate with each other. Each container has a port. This port is used to communicate with other containers.

 

  • What is Docker Namespace?

Answers. In containers, the namespace adds an isolation layer. It is a Linux feature. To safeguard the underlying host system and make it portable, it provides many namespaces. PID, IPC, Mount, User, Network are the names of a few Namespace supported by Docker.

 

  • State the lifecycle of a Docker Container.

 

  1. Creation of a container 
  2. Running of the container 
  3. Pause the container 
  4. Un-pause the container. If paused
  5. Starting of the container
  6. Stopping the container 
  7. Restart the container 
  8. Kill the container 
  9. Finally, the destruction of the container 

 

  • What is Docker Machine?

 

Answers. A tool that enables the user to install Docker Engine is called its Machine. This installation is done on a virtual host. These virtual hosts can be managed using commands from the machine. This machine also allows the provision of Swarm Clusters.

 


Wednesday, 28 October 2020

DATA SCIENCE INTERVIEW QUESTIONS AND ANSWERS WELL EXPLAINED

 


If you want to know more related to the vital data science interview questions, you can go through this blog. It provides all the questions along with the precise and explained answers that one might need for the interview preparation. It is an incredible option to learn more about each aspect associated with data science. The demand for data science has been increasing exponentially over the years. 

 

This blog comprises of all the important questions that any applicant can use to crack the data science interview. Besides, one must understand all the basic concepts and the terminologies to properly prepare for the exam. These interview questions will help you a lot.

 

Data Science Interview Questions

 

Given below are lists of most frequent data science interview questions that a Data science adherent should know: These Interview questions will really help you.

 

Data Science Interview Questions and Answers: Unsupervised and Supervised Learning

The mixture of numerous algorithms, tools, and principles of machine learning along to search the patterns that are hidden from the raw information/data. 

The differences are as follows:

Supervised learning:

  • The labeling of the input information is done. 
  • Training data set is implemented
  • It makes use of prediction. 
  • It allows regression and regression. 

Unsupervised learning: 

  • The input information or data is not labeled. 
  • The collection of input data set 
  • Can be used for further learning
  • It permits dimension minimization, density estimation, and classification.

 

2. Selection Basis - explained 

 

The error that persists while the researcher chooses who is supposed to be studied is known as the selection bias. It is normally related to the research and it is not on the criterion of selection of the applicants in a random way. It is also often known as the selection effect—the misrepresentation of the statistical learning, due to the method of gathering samples. In case it is not taken into consideration, then a few of the deductions of the study might not be precise. 

 

The variety of selection bias might comprise of the following:

 

  • Sampling bias: This error is considered to be systematic since it is caused by a sample of the population that is non-random. Due to this, some of the population members will not be included than others which leads to a biased sample. 
  • Time Interval: The termination of the trial might occur early at an extreme value. However, the variable with the largest variant might reach the extreme value, even when each variable contains a similar mean. 
  • Attrition: The selection bias that occurs due to attrition is referred to as attrition bias. 
  • Data: When particular data subsets are selected to support a rejection or deduction of bad data on random grounds rather than previously specified decided criteria

 

3. Confusion matrix - Explained

 

The 2X2 table that comprises a total of 4 outputs offered by the binary classifier is known as the confusion matrix. Several types of measures like accuracy, error-rate, specificity, precision, Sensitivity, and recall are all obtained from it. Besides, the test data set refers to the set of data that is utilized for the evaluation of performance. 

 

6. Explain a bias-variance trade-off. 

 

Bias: The error that occurs in your model as a result of the generalization of the machine learning algorithm is known as bias. It can result in underfitting. For you to understand the target function easily, it makes simplified assumptions at the time when one trains the module. 

 

Variance: The error that occurs in your model as a result of the over-complexity of the machine learning algorithm. Besides, from the data set training the module learns noise and does poorly on the data set test. Overfitting and high sensitivity can be a result of this.

 

Besides, if you raise the model complexity, you will notice a minimization in error because of the lower bias in the model. However, it can only occur until a specific point. However, if you keep raising the complexity of your model, it will lead to over-fitting of your model. Therefore, your model will end up suffering from high variance. 

 

Bias-Variance trade-off: The primary objective of any super surveillance machine learning algorithm is to conquer a low bias aligned with low variance. The data science companies are looking for Data scientists who have Certifications. Since it is necessary for attaining enhanced prediction performance. 

 

  • The high variance and low bias are the elements contained in the support vector machine learning. However, do you know that it is possible to modify the C – parameter by rising by trading which will impacts the number of violations of the margin. And it is enabled in the data related to training which results in the rise of the bias but reduces the variance.
  • The k-nearest neighbor algorithm usually comprises of high variance and low bias. The value of k can be raised to change the trade-off. It then raises the number or quantity of neighbors that contribute to the estimation and thus, raises the bias of the module/ 

 

The relationship amidst the variance and bias in machine learning is inevitable. Raising the bias will reduce the variance, whereas raising the variance will reduce the bias. 

 

Data Science Interview Questions - Explained

 

5. Understanding normal distribution

 

There are various in which data can be distributed with a bias to the right or the left or it can be all mixed-up. 

Although there are several chances that the data is spread around the central value without the presence of any bias to the right or left, besides, the normal distribution can also be reached in the bell-shaped curve form. The random variables are spread in the bell-shaped curve form. 

The normal distribution properties are as follows:

  • Unimodal – single mode
  • Symmetrical – right and left parts/halves are mirror images
  • Bell-shaped – the maximum mode present at the mean
  • The Center part consists of the mean, median, and mode 
  • Asymptotic

 

6. State the meaning of covariance and correlation in statistics.

 

The two types of mathematical concepts or approaches that are used extensively in statistics. The relationship is recognized by both the covariance and correlation. Besides, it also measures/evaluates the reliance between any two random variables. Even though work is alike between these concepts, but they hold a different meaning from each other. 

 

Correlation: The method utilized for measuring and as well as predicting the quantitative connection/relationship between the two random variables is referred to as correlation. It is generally used for measuring the strong connection between the variables. 

 

7. Explain both the confidence interval along with the point estimates. 

 

The value given by the post estimation is specific as a prediction of a population parameter. To obtain the Point estimations for the population parameters, the methods used are moments and maximum likelihood. 

 

 

The population parameters are potentially identified by the range of values derived by the interval of confidence. This interval is normally selected because it informs us about the possibility or likelihood of this interval is to include the population parameter. This possibility or probability is referred to as confidence level or coefficient and is signified by 1 – alpha; the level of significance is the alpha here. 

 

8. State the objective of A/B testing. 

 

The hypothesis test that is done for a random experiment with the variables A and B is referred to as A/B testing. The key objective behind this testing is to determine any changes to the web page to enhance the desired result. It is an incredible method that is used for identifying the top marketing and advertising plans or strategies for your business. Besides, you can use this to test anything from sails emails to a copy of websites to look for ads. 

 

9. Describe the p-value.

 

A p-value can be used while performing a hypothesis test in statistics to identify the results' strength. The number that falls between 0 and 1 is known as the p-value. The strength of the result is represented depending on the value. The on-trial claim is referred to as the null hypothesis. 

 

The strength against the null hypothesis is determined by the low p-value. It indicates that you can decline/reject the null hypothesis. The strength of the null hypothesis is determined by the higher p-value, which indicates that it is possible to receive the null hypothesis p-value. With the higher values, the data are probably with a true null whereas the p-value is low then the data are probably not with any true null. 

 

10. Is it possible to generate any number that falls between 1-7 randomly with only a single die?

 

  • The die comprising of six sides from 1-6. However, there is no even result that one can get from rolling the die for a single time. In case the die is rolled twice then it can be termed as the event for two rolls. Therefore, we get around 36 dissimilar or a variety of outcomes. 
  • To obtain 7 equal outcomes, it is essential to minimize the 36 to any number divisible by 7. Therefore, only 35 outcomes/results can be included out of 36. 
  • For instance, taking out the combination like (6,6) which means if 6 appears two times, you must roll the die again. 

 

  • In a way, each of the 7 sets of outcomes is considered to be equal probably. 

 

11. Define the statistical power of Sensitivity and the method through which it can be calculated. 

 

The precision of a classifier (SVM, logistic, forest, random, etc.) is verified with the use of Sensitivity. The “predicted true events/ total events” are referred to as Sensitivity. The true events are known as true events and also predicted true by the model. 

The seasonality calculation is quite direct. 

 Seasonality = (True positives)/ (Positives in Original Dependent Variable)

 

12. Reason for performing resampling.

 

The situations in which resampling is done are as follows:

 

  • The precision of the sample statistics is evaluated or measured by making use of the available information or random drawing with replacement from a collection of data points. 
  • While executing a particular test, the labels are substituted on data points.
  • There are several random subsets used to validate the models. 

 

13. What do you mean by under-fitting and over-fitting?

 

In machine learning and statistics, the most important and normal activity is fitting a model into a collection of training data. Thus, it helps in creating dependable and trustable estimation or predictions on the untrained data. 

 

Overfitting: A noise or an issue that occurs randomly is defined by the statistical model. This happens only when the module is extremely complex; for instance; it has numerous parameters as compared to the observation numbers. The performance of the overfitted model is normally poor predictive since it reacts excessively to the small fluctuations in the training data. 

 

Underfitting: If the statistical model or machine learning algorithm is unable to seize the original data trend, underfitting occurs. In case the non-linear model is fitted to the linear model, underfitting occurs. The performance of this type of model is also very least predictable.

 

14. Define how you can deal with underfitting and overfitting as a data scientist?

 

To deal with underfitting and overfitting, the data can be resampled to predict the precision of the model. Also, by acquiring a set of data that is valid to measure or evaluate the model.

 

15. State the meaning of regularisation and its usage. 

 

The process done by data scientists also of the tuning parameter to a model to induce evenness or smoothness to avoid overfitting is known as regularization. It is normally completed by the addition of a constant multiple to a weight vector that is present. L1(Lasso) or L2(ridge) is frequently the constant. The loss function that is calculated on the training set that is regularized should be minimized by the model predictions. 

 

16. Explain the Large Number Laws. 

 

The theorem that defines the outcome of executing a similar experiment numerous times. The foundation of frequency-style thinking is formed by this theorem. The sample variance, sample standard deviation, and the sample mean unite to what they are trying to predict. 

 

17. What do the confounding variables mean?

 

The variable that impacts both the independent and the dependant variable in statistics is known as confounding variables. 

 

For instance, you are exploring if no constant exercise increases weight, 

 

No exercise = independent variable

 

Increase in weight = dependent variable

 

The variable that influences these variables is referred to as the confounding variable, such as the subject age.

 

18. Name the bias types that can happen during sampling. 

 

  • Survivorship bias
  • Under coverage bias
  • Selection bias

 

19. Define the Survivorship Bias.

 

The logical error related to concentrating on the features that assist in surviving some methods and normally neglecting the ones that failed to work due to the absence of importance

 

20. Explain Selection Bias

 

If the sample attained does not represent the population planned to be analyzed, it might result in the occurrence of selection bias. 

 

21. Define the working of a ROC curve.

 

The representation of the contrast between the false-positive rates and the true positive rates at numerous thresholds with the graph is referred to as the ROC curve. The ROC curve is used to the trade-off between the false-positive rate and the Sensitivity.

 

22. TF/IDF vectorization - Explained

 

The numerical statistic that demonstrates the importance and value of a word to a document in a set or corpus is referred to as TF-IDF. In-text mining and data retrieval processes it is utilized as a weighing aspect. 

However, it is offset by the word frequency in the corpus; it is useful to assist in adapting to the aspect that few words appear more often normally. 

 

Data Science Interview Questions – Data Analysis

 

Given below are the data science interview questions that are mostly related to data analysis:

 

23. For text analytics, what will you choose R or Python?

 

Python must be selected for the reasons given below: 

 

  • A python is an incredible option since it comprises of Panda library that offers simple and effortless to use information/data structures and high-efficiency data performance tools. 
  • R is more useful in the case of machine learning rather than text analysis. 
  • For every kind of text, analytics python is considered to perform quicker. 

 

24. What is the role of data cleaning in the process of analysis?

 

Data cleaning is extremely useful in the analysis due to the following reasons:

 

  • Data scientists and data analysts normally work with the format that is usually converted with the help of several sources used to clean the data.
  • The precision of the model in machine learning is enhanced with the help of machine learning. 
  • It is a lengthy method because, since the amount of the sources of data increases, it also increases the time used to clean the data rapidly. It happens due to a large number of sources along with the amount of data these sources create/generate. 
  • Cleaning data itself might take over 80% of the time, which makes it a vital part of the analysis process. 

 

25. What do you mean by multivariate, univariate, and bivariate analysis?

 

Univariate analysis: The descriptive statistical analysis methods that can be distinguished depending on the quantity/number of variables included at any fixed time. 

 

Bivariate analysis: The separation between two types of variables at a single time is understood through the bivariate analysis. For instance, examining the sale volume along with spending is known to be a bivariate analysis example. 

Multivariate analysis: To learn the results of variables on the responses, two or more variables are studied, which is referred to as multivariate analysis. 

 

26. Define the Star Schema. 

 

The data set includes a central table along with a traditional data set. The IDS are mapped to the physical descriptions or names using the satellite table. It can also be linked to the central table with the help of the ID field. Besides, these tables are referred to as lookup tables and are extremely useful for real-world applications since they conserve a huge amount of memory. Many-a-times, there are numerous layers of summarization included in the star schemas to retain the data quickly. 

 

Data Science Interview Questions – Deep Learning 

 

Given below are a few of the top data science interview questions related to deep learning:

 

27. Explain both deep learning and machine learning. 

 

The ability that enables computers to learn without the need to be programmed is referred to as machine learning. There are three kinds of classification of it, such as:

  • Learning with a reinforcement 
  • machine-learning goes unsupervised
  • machine-learning under supervision

The subdivision of machine learning that is associated with algorithms enthused by the function and structure of the artificial neural networks, i.e. the brain

 

28. Why is deep learning being used widely all across the world?

 

 Nowadays, deep learning is increasing in popularity over the years, although it drastically took up one of the leading spots recently. The reasons are as follows:

  • The exponential growth of the generation of data due to several sources. 
  • The increase in the hardware resources essential to run these models smoothly. 

Using GPU, it is possible to create deeper and larger deep learning models, and they are extremely quick as well. Besides, it takes a lower amount of time as compared to the previous methods. 

 

29. Define reinforcement learning. 

 It is the processes through which you can learn how to map the circumstances and what is needed to be done to action. The end outcome is to make the best use of the numerical reward signal. Although it does not indicate exactly which action one must take rather, you must search for the action that offers the best results. 

 

30. State the usage of weights in networks. Continue Reading

Tuesday, 27 October 2020

LINUX INTERVIEW QUESTIONS AND ANSWERS WELL EXPLAINED

 


Linux is a type of open-source OS. OS stands for Operating System. Let’s have a detailed explanation of Linux Interview Questions and answers. The operating system defines the particular software which helps in the direct management of hardware as well as the resources of the system. The operating system has its position between the hardware and applications.

It makes a connection between the complete software and physical resource responsible for performing the work. OS can be seen as a car engine that does not need any support to run, but it becomes meaningful when associated with an operating car. It acts as the core factor which binds all the other elements, and without its help, no work can be done. 

Linux Interview Questions Part 1- Working of the Linux

Initially, Linux was designed to work the same way as UNIX, but the operating system evolved with time and is compatible with a wide range of hardware from supercomputers to smartphones. Linux kernel is present in every OS based on Linux. This Linux kernel is useful in managing the hardware resources and several software packages constituting the remaining part of the operating system. 

There are a few core components common to the operating system, like GNU tools. These particular tools are of great benefit to the user in managing the resources rendered by the kernel. The tools also help the users to install other software as well as configure different security settings and performance. These tools also play a few more functions other than the above-mentioned ones. Together, these tools form a functional and complete operating system. The different combinations of various software in Linux distributions can vary as the operating system is open source. 

The different pieces of the Linux operating system are:

  1. Boot loader – Boot loader is the software behind the management of the boot process of a certain computer. It normally comes as a pop-up on the screen and then goes back to boot in the operating system for many users. 
  2. Kernel – The kernel is a piece of the complete system known as Linux. It manages the memory, peripheral devices, and central processing unit of the system. The kernel works as the core part of the system. It represents the lowest step of the operating system. 
  3. Init system – The init system is a sub-system and helps in bootstrapping the userspace. It is in charge of controlling the daemons. System D is one of the widely known init systems. The init system manages the boot process after initial booting is done by the bootloader. The bootloader is otherwise called Grand Unified Bootloader or the GRUB. 
  4. Daemons – The daemons refer to the background service that starts during the boot or after the user logs in to the system. The background services include printing, scheduling, and other system facilities.
  5. Graphical server – The sub-system behind displaying graphics on the desktopscreen is the graphical server. The x server is the common name used to denote the graphical server.
  6. Desktop environment – The desktop environment is the part of the system that the user interacts with. Many desktop environments are available for the user to choose from. The desktop environment consists of built-in applications like file manager and another desktop program.
  7. Applications – The full array of applications is not offered by desktop environments. Linux offers its clients much high-quality software which can be installed quite easily. The modern distributions of Linux have tools like app stores which help in centralization and simplification of the installation process. 

Linux Interview Questions and Answers Part 2: Reasons to choose Linux.

Many people have doubts about choosing the Linux operating system over others. The question comes as to why to choose a computing environment that is completely different while the other operating systems available work pretty well.

To answer this question, another question can arise that is the prior operating system good enough to fight the virus, malware threats, and various system crashes. Linux operating system is the best platform to fight these hindrances. 

Linux defined its place as the most reliable and versatile computer ecosystem that can help the users in many ways. It gives many opportunities for individuals to create the best experience with an amazing computer environment.

Linux comes as the best solution for almost all desktop or computer problems. It provides reliability as well as zero entry cost. Any user can install Linux in their computer system without having to pay a single penny. Software programming or server licensing does not cost anything to the user. 

We can compare the server cost of Linux with Windows 2016. The price of a Windows 2016 server is $882.00 for the standard edition. The above-mentioned cost does not take into account the Client Access License and other necessary software licenses.

But the Linux server is free and very easy in terms of installation. This zero-cost facility wins over many customers. The operating system provides great benefits to the user in terms of cost as well as performance.

People using Linux also give great positive comments now and then. According to them, they have never faced any sort of problems relating to ransomware or other viruses. The operating system is not very vulnerable to virus and malware attacks.

In case the kernel is updated, a server reboot becomes necessary. It is normal for the Linux server to be fine without multiple reboots within less period. Stability and dependence of the operating system are maintained practically if the updates are done when recommended. 

Open source feature- Linux

The open-source license is the governing agent for Linux distribution. The key features of an open-source are as follows:

  • There is freedom in case of running the program let it be for any need.
  • There is also freedom for studying the working of the program, and necessary changes can be done according to individual wishes. 
  • The user also gets to help the neighbors by redistributing the copies. 
  • The user can even distribute the modified version to others by making copies of it. 
  • It is very important for every person who works with the Linux platform to understand the above-mentioned points for their benefit. Linux is so far the best operating system as informed by the users.
  • The freedom that Linux gives as an open-source operating system is one of the major reasons why most people prefer Linux over another operating system. It gives them freedom of choice as well as use. 
Installation of Linux

Few people think that the installation of the operating is system is a very difficult task. But Linux helps the people in this situation by providing an easy installation facility. Almost all Linux versions have live distribution. Live distribution help in running the operating system with the help of both CD or DVD and USB flash drive.

 This helps the user to carry on the work without having to change anything in the hard drive. There is complete functionality available at the time of installation. The process of installation is pretty simple, and the instructions flash at the time of practical use.

There is the presence of an installation wizard which helps the user to get through the complete process in a step by step manner.

The first step involves preparation. In this step, the machine meets the installation requirements of the Linux operating system.

 Then the process of wireless setup follows where the connection of network takes place to manage and download various updates. The next step involves hard drive allocation, where the installation of the Linux operating system takes place along with a different operating system. This process denotes the dual booting of the system.

 Then in the next step location is set in the map, and the user decides the keyboard layout. The final step is setting up a username and creating a password for the system. 

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