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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

Most Commonly Asked Rest APIs Interview Questions

Vibha
Vibha Last Updated : 04 Nov, 2022
4 min read

This article was published as a part of the Data Science Blogathon.

Introduction

Rest API

Representational State Transfer (REST) is an architectural paradigm for designing online applications based on the Hypertext Transfer Protocol (HTTP).

A REST API, often known as a RESTful API, adheres to REST principles. In a REST API, all data are represented by a unique, consistent resource identifier and processed as resources (URI). The Twitter API, for instance, makes each tweet a resource that clients can access. Using Twitter’s API, clients may publish tweets and do other operations on the site.

REST’s development has made APIs more accessible while revealing their strength and potential. REST APIs are simple to create and cacheable with a resource-oriented architecture. In addition, RESTful APIs have spawned important developments such as microservices architecture and cloud computing. Therefore, it is not surprising that REST API developers are in high demand today, given that they provide a competitive advantage to businesses that employ RESTful services.

Interview Questions on Rest API

1. What is your understanding of RESTful Web Services?

RESTful web services are those that adhere to the REST architecture. REST stands for Representational State Transfer and is implemented using the HTTP protocol (web protocol). These services are lightweight, provide maintainability and scalability, and communicate between applications written in various programming languages. Utilizing request headers, request body, response body, status codes, etc., they provide the client access to server-hosted resources via the web browser.

2. What does it mean to be “statelessness” in REST?

The REST architecture is known as stateless because it does not keep any client session-related data on the server. In essence, session states are maintained solely on the client’s side. Effectively, this is known as statelessness.

It guarantees that the server cannot exploit any saved information. However, since no data are kept, each request from the client to the server must include all the required request information.

3. What are some significant REST characteristics?

Some of the most important characteristics of REST are:

  • Since REST is stateless, the SERVER has no state.
  • With a properly implemented REST API, the server might be restarted between calls because all data is sent to it.
  • Web services typically use the POST method to perform actions, whereas REST utilizes the GET method to retrieve resources.

4. Can you outline the cons of RESTful web services?

The cons are:

  • As the services adhere to the concept of statelessness, sessions cannot be maintained. Client-side session simulation is responsible for passing the session id.
  • REST does not impose inherent security constraints. It inherits the security precautions of the implementing protocols. Implementing security measures, such as integrating SSL/TLS-based authentications, etc., must be done with caution.

5. Can transport layer security (TLS) be implemented in REST?

Indeed, we can. TLS is responsible for encrypting the REST client and server communication and authenticating the server to the client. As the successor of the Secure Sockets Layer, it is utilized for secure communication. HTTPS is compatible with both TLS and SSL, making it useful for creating RESTful web services. Notably, the REST protocol inherits properties from the protocol it implements. Therefore, security measures depend on the REST protocol implementation.

6. What are the pros and cons of statelessness in REST APIs?

Pros of statelessness:

  1. Because it has no session-related requirements and may be implemented on any server, statelessness enables the scalability of APIs to millions of concurrent users.
  2. The server knows “where” each client is in the application since all pertinent data is transmitted with each request.
  3. Statelessness simplifies REST APIs by eliminating any server-side synchronization complexity.

Cons of statelessness:

  1. A substantial amount of supplementary data must accompany each customer request.
  2. This repetitive data transmission might reduce network efficiency
  3. Being stateless further lowers server-side control over the behavior of the program.

7. What is ‘Addressing’ in RESTful WEB services?

The method of identifying server resources is known as addressing. URLs are used to address these resources in RESTful web services. These addresses may relate to a single resource or several resources.

:////

8. What do you mean by Idempotent methods?

Idempotent methods deliver the same result regardless of the times the same request has been performed.

These approaches are essential due to the frequent occurrence of client-side duplicate request transmission. Therefore, it is essential to employ Idempotent procedures to prevent such mistakes.

9. How are REST and SOAP different?

REST (Representational State Transfer)

  • It is a design pattern used in the development of online services.
  • It is more efficient and cacheable.
  • It inherits just the security protections that have been applied for the protocol.

SOAP (Simple Object Access Protocol)

  • It is a tight protocol used to construct APIs with security.
  • It has a slower speed and cannot be cached.
  • It has the ability to establish its own security mechanisms.

10. What are the differences between AJAX and REST?

AJAX

  • Using the XMLHttpRequest object, requests are delivered to the server. Later, Javascript reads the answer and dynamically modifies the page.
  • Because AJAX permits asynchronous queries, it does not necessitate continual client-server interactions
  • AJAX dynamically refreshes the user interface without page refreshing

REST

  • REST accesses resources using a URL and a request/response pattern.
  • REST demands a continual client-server interaction
  • REST requests and updates data or information from a server

11. What are the main security problems that web services face?

Since online services frequently deal with a great number of sensitive data, application security becomes a big problem. Listed below are a few things to bear in mind.

  • Encryption – A web service may have numerous apps and may contain a vulnerable node. Therefore, it is prudent to encrypt data so that they stay private at all costs.
  • Authentication – Authentication is a challenge that occurs when managing a big user base. Authentication prevents unauthorized access to user data and allows you to monitor user behavior.

Conclusion

We practiced answering the most common interview questions on RESTful web services. In recent years, REST APIs have emerged as a significant technology in the software development field. Scalable and easily maintained RESTful web services are hard to come by, but their creators are masters of the art. The need for developers versed in RESTful web service development is expected to rise significantly in tandem with the industry’s rising use of REST architecture. Key takeaways from the article:

  • REST stands for Representational State Transfer
  • The evolution of REST has made APIs more approachable while showing their true power and potential.
  • Idempotent methods provide the same response regardless of how many times the same request has been executed.
  • Identifying server resources is referred to as addressing.

This blog post should help you with REST API interview questions. These REST API interview questions can help you ace your next interview.

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blogathonData Engineeringinterview questionsREST API
Vibha
Vibha

I am an engineering student. Currently, I am pursing Btech from Vellore Institute of Technology. I am very passionate about programming and constantly eager to expand my knowledge in Data Science and Machine Learning.

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