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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

30+ Shell Scripting Interview Questions

Mounish V
Mounish V Last Updated : 19 Jul, 2024
4 min read

Introduction

Shell scripting is a fundamental skill that helps system administrators and developers to streamline tasks. It helps them effectively manage systems and handle files efficiently within a command-line environment. This skill is vital for technical interviews in the IT and development fields. So, here are some shell scripting interview questions, that will prepare you well for your interviews and enhance your proficiency.

Shell Scripting Interview Questions

Here are the 31 most commonly asked interview questions on shell scripting.

Q1. What is a shell script?

Ans. A shell script is a script written for a shell, or command-line interpreter, typically for automating administrative tasks and system management.

Q2. How does shell scripting differ from programming?

Ans. Shell scripting automates administrative tasks and system processes, while programming focuses on creating applications with complex logic.

Q3. Explain the difference between single and double square brackets in shell scripting.

Ans. Single square brackets `[ ]` are a POSIX compliant test command, while double square brackets `[[ ]]` are an enhanced version with additional features such as pattern matching and string manipulation.

Q4. How do you comment out multiple lines of code in a shell script?

Ans. Multiple lines can be commented out by enclosing them between `: ‘` and `’`, or `<<COMMENT … COMMENT`.

Q5. What is the purpose of the `chmod` command in shell scripting?

Ans. The `chmod` command changes the permissions of a file or directory, allowing or denying specific users’ read, write, and execute permissions.

Q6. How can you pass arguments to a shell script?

Ans. Arguments can be passed to a shell script by listing them after the script name when executing it, for example, `./script.sh arg1 arg2`.

Q7. Can you describe the purpose of the shebang line?

Ans. The shebang line, found at the script’s beginning, identifies the script interpreter’s path and is used to run the script.

Q8. What are some standard streams in Linux?

Ans. Linux provides three standard streams: Standard Input, Standard Output, and Standard Error.

Q9. What alternative command can replace echo?

Ans. The command tput can be used instead of echo, giving control over output display formatting on the screen.

Q10. What do you know about GUI scripting?

Ans. GUI scripting involves using a graphical user interface to manage computer operations and applications, relying on the operating system for support.

Shell scripting interview questions

Q11. How does a shell variable function?

Ans. Shell variables are fundamental in shell scripts, enabling the storage and manipulation of data, typically stored as string variables.

Q12. What are the disadvantages associated with shell scripting?

Ans. Shell scripting has several drawbacks: it starts slowly but speeds up, is not easily portable, can cause data loss due to typing errors, and poor designs can be costly and disruptive.

Q13. What does the Crontab command do?

Ans. Crontab, or cron table, refers to a list of scheduled commands managed by the cron job scheduler, used to execute tasks at regular intervals.

Q14. What stages does a Linux process go through?

Ans. A Linux process has four stages: Waiting (for resources), Running (currently executing), Stopped (post-execution), and Zombie (active in process table but stopped).

Q15. Explain the role of metacharacters in a shell?

Ans. Metacharacters are special characters in a shell that provide information about other characters. For instance, using ls s* lists all files starting with ‘s’.

Q16. How can you connect to a database server in Linux?

Ans. Use the isql utility, included with the open client driver, to connect to a database server:

isql –S serverName –U username –P password

Q17. What is the purpose of a superblock in shell scripting?

Ans. A superblock contains vital records of a specific file system, such as size, counts of filled and empty blocks, usage information, block groups’ size, and inode tables’ location and size.

Q18. What do you understand by a shell in computing?

Ans. A shell is a command-line interpreter that converts user-entered commands into a form understandable by the kernel, executing the required programs.

Q19. What is the significance of the ‘$#’ variable in shell scripting?

Ans. The ‘$#’ variable displays the total number of arguments passed to a script.

Q20. Differentiate between $@ and $* in shell scripting.

Ans. $* treats all positional arguments as a single string, whereas $@ treats each quoted argument separately.

Q21. What common shells are used in a Linux environment?

Ans. Common Linux shells include the C-Shell (e.g., TENEX C-Shell, Z-Shell) and Bourne Shell (e.g., Korn Shell, POSIX Shell, Bourne-Again Shell).

Q22. Can shell scripts be executed on different Unix-like systems?

Ans. Yes, though there may be differences in shell syntax and commands across systems.

Q23. How many kernels and shells exist in a UNIX environment?

Ans. In UNIX, multiple shells can exist, but there is only a single kernel.

Q24. How do you append output to a file in a shell script?

Ans. Use the >> operator to append output to a file. Example: echo "text" >> file.txt.

Q25. What are special variables in shell scripting?

Ans. Special variables include $0 (script name), $# (number of arguments), $* (all arguments), and $@ (all arguments as separate strings).

Q26. How do you debug a shell script?

Ans. Enable debugging with the -x option, which prints each command before execution. Example: bash -x scriptname.sh.

Q27. What is the purpose of awk in shell scripting?

Ans. awk is a powerful text processing and data extraction language used within scripts.

Q28. How can you find and replace text in a file using shell scripting?

Ans. Use the sed command to find and replace text. Example: sed 's/oldtext/newtext/g' file.txt.

Q29. What does the eval command do in shell scripting?

Ans. The eval command executes arguments as a shell command.

Q30. What are some common use cases for shell scripting?

Ans. Automating backups, system monitoring, batch file processing, and user account management.

Q31. What are the best practices for writing shell scripts?

Ans. Use meaningful variable names, add comments for clarity, handle errors, and avoid hard-coding values.

Conclusion

One skill that greatly increases the productivity of both developers and system administrators is shell scripting. It guarantees efficient system administration, streamlines processes, and manipulates files with skill via command-line interfaces. Technical interviews in the IT and development domains require proficiency in this area. Knowing these 31 crucial shell scripting interview questions will help you prepare, increasing your preparedness and shell scripting proficiency.

Mounish V
Mounish V

I'm a tech enthusiast, graduated from Vellore Institute of Technology. I'm working as a Data Science Trainee right now. I am very much interested in Deep Learning and Generative AI.

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