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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

10 Web API Interview Questions and Answers (2024)

Vibha
Vibha Last Updated : 01 Feb, 2024
5 min read

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

Introduction

Web API is a vital application programming interface that enables web services across various browsers and devices, including tablets, mobile phones, etc. Its lightweight and streamlined services and its usability as a stand-alone web service application are of great importance.

web api interview questions
Source: Medium

Suppose you are a Web API student or a professional building your skills in this framework. In that case, you are well aware of the importance of accessing a dependable repository of interview questions. Due to its practical nature, every collection of questions must address its implementation in real-world circumstances. These interview questions might also assist you in preparing for your ideal career in this industry.

Let’s gain a deeper grasp of the concept by examining these Web API Interview Questions, which will aid you during a job interview or just improve your general knowledge and understanding of this topic.

Table of contents

Web API Interview Questions

Q1. Why is Web API so important?

Web API is typically regarded as a service that gives us data or information from the server. It is very important for the following reasons:

Web API Interview Questions
  • It provides a data access interface for websites and client applications.
  • It may be used to retrieve data from and save data to the database.
  • It supports several text formats, including XML, JSON, etc.
  • It is appropriate or compatible with every browser and every device, including mobile, desktop, online, etc.
  • It uses low-bandwidth data formats, like XML or JSON, and is, therefore suitable for devices with limited bandwidth, like smartphones.

Web API is more relevant for UI

Q2. What differences exist between Web API and WCF REST API?

Web API is appropriate for HTTP-based services, but WCF REST API is optimal for Message Queues, one-way messaging, and duplex connectivity. WEB API accepts every media format, including XML and JSON, while WCF supports SOAP and XML. WCF is great for establishing service-oriented applications, while ASP.Net Web API is ideal for providing HTTP services. It requires no configuration to operate, but WCF requires a notable amount of configuration.

Q3. What are the different Web API return types?

An action of a controller can return the following values:

  • Void – It will display empty
  • HttpResponseMessage – This function converts the response into an HTTP message.
  • IHttpActionResult – invokes ExecuteAsync internally to generate a HttpResponseMessage.
  • Other types – The serialized return value can be written into the response body.

Q4. What are the different HTTP Methods?

There are a variety of HTTP methods that may be used depending on the situation. These techniques are case-sensitive and need capitalization.

  • GET – The GET method retrieves or gets information from a server using a specified URI.
  • HEAD — This is identical to the GET method but only transports the status line and header section.
  • PUT- It is used for updating and replacing all existing resources with the submitted material.
  • POST – A POST request is used to transmit data to a server.
  • DELETE — Deletes all current resources specified by a URI.
  • OPTIONS – Describe the target resource’s communication options.
  • CONNECT – Establishes a tunnel to the specified server.
  • TRACE — Performs a message loop-back test along the path to the resource being targeted.

Among these processes, The most frequent methods are GET/PUT/POST/DELETE.

Q5. What is Web API version 2.0?

It’s mainly an improved and upgraded capability. This new version supports the following new features:

  • New Routing Properties
  • Securing the ASP.NET Web API with OAuth 2.0
  • CORS support for Cross-Origin requests
  • IHttpActionResult is the return type for HTTP actions.
  • $expand and $select support in OData Service

Web API 2.0 is regarded as an excellent choice and an acceptable development model due to its new capabilities, which make it easier to build RESTful service interfaces for clients running on diverse platforms. Additionally, routes can be configured at the Web API function or controller level.

Q6. What do you mean by SOAP?

SOAP is an XML messaging format used in interactions between online services. It lets the transmission of messages over HTTP or JMS, but additional transport protocols are supported too. It is a communications protocol also based on XML for exchanging data between computers

Q7. When should ASP.NET Web API be selected over WCF?

It relies entirely on necessity. Choose ASP.NET Web API if you want solely HTTP-based services, as its lightweight architecture makes it suitable for devices with low bandwidth. WCF may be used to establish REST services too, but this involves extensive setup. WCF is the superior choice if you need a service that supports several transport protocols, like HTTP, UDP, TCP, etc.

Q8. How do we deal with Web API errors?

Error and exception may be accomplished using the following classes:

  • Implementing HttpResponseException – This exception class contributes to returning the HTTP status code provided in the exception Constructor.
  • Implementing HttpError – This exception class facilitates the return of a relevant error code to the client as a HttpResponseMessage.
  • Implementing Exception filters – Exception filters aid in capturing unhandled exceptions or errors issued  may be used whenever a controller action call generates an unhandled exception.

Q9. Can you provide me with an example of Web API Routing?

Config.Routes.MapHttpRoute( name: “MyRoute,”//route name routeTemplate: “api/{controller}/{action}/{id}”,//as you can see “API” is at the beginning. defaults: new { id = RouteParameter.Optional } );

Q10. Describe XML and JSON.

XML (Extensible Markup Language):

  • It is intended specifically to store and convey data.
  • It is comparable to HTML but more flexible than HTML because users can create their own tags.
  • It is used to represent structured data like documents, data, and configuration.

JavaScript Object Notation (JSON):

  1. It is a
    lightweight data storage and transmission format.
  2. It is a
    standard text-based format used to express structured data based on
    JavaScript object syntax and is easy to comprehend.
  3. It is quicker and simpler to use.

Conclusion

In summary, grasping Web API interview questions is vital for success in web development job interviews. It guarantees proficiency in essential concepts such as REST, HTTP methods, and data formats. Practising with these questions allows candidates to demonstrate their expertise, increasing their chances of securing opportunities in the field.

  • Web API provides web services across browsers and devices, like tablets, smartphones, etc
  • It supports several text formats, including XML, JSON, etc.
  • Web API 2.0 makes it easy to establish RESTful service interfaces for clients on multiple platforms.
  • XML stands for Extensible Markup Language, and JSON stands for JavaScript Object Notation

We hope this blog article has helped you familiarize yourself with frequently requested web API interview questions. These commonly requested interview questions can help you hone your skills and ace your next interview.

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blogathoninterview questionsWeb 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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