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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

Top 5 Cloud Computing  Interview Questions (2025)

Shikha
Shikha Last Updated : 18 Dec, 2024
7 min read

Introduction

Cloud computing, an internet-based emerging computing paradigm, offers a revolutionary way to deliver a spectrum of computing resources. From databases and applications to analytics, servers, and networking, it encompasses a wide array of services. The beauty of cloud computing lies in its provision of automation and standardization, simplifying the utilization of computing resources for users. Moreover, with its pay-as-you-go model, cloud computing not only facilitates cost reduction but also enhances operational efficiency, making it an indispensable tool for modern infrastructure. Are you looking for more insights on cloud computing? Feel free to explore our comprehensive cloud computing questions and answers section for further clarity and understanding.

cloud computing interview Questions

In this blog, I discussed Cloud Computing Interview Questions that will help you to set a pace for cloud computing and ace your upcoming cloud interview!

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

5 Cloud Computing  Interview Questions

Q1:- What are the Different Cloud Delivery Models?

Ans:- Models which are used to represent the computing environments are termed cloud delivery models. There are mainly three types of cloud delivery models present in the market, which includes SaaS, IaaS, and PaaS. Every model has a unique set of benefits that makes it different from others in terms of traits and usages.

Let’s go deeper with each one of them!

  • Software as a Service (SaaS): Software as a Service (SaaS) is a service delivery paradigm that is highly related to Application Service providers (ASP) and on-demand computing software delivery models. It provides a cloud platform where a customer’s application is created and hosted by a third-party provider in a multi-tenant model. SaaS enables the usage of those applications over the internet, and no additional installation is needed.

    Some common examples of SaaS applications used for business technologies are email, Customer relationship management (CRM), sales management, human resource management (HRM), financial management, billing, and collaboration.

  • Infrastructure as a Service (IaaS): Infrastructure as a Service (IaaS) is one of the three main categories of cloud computing services that provide physical and virtual computing resources over the internet. IaaS enables firms to access the hardware resource without even purchasing them. Infrastructures provided by IaaS include the operating system, database storage, networking, and many other utility software elements based on demand.

  • Platform as a Service (PaaS): Platform as a Service (PaaS) is an important category of cloud computing services that provides a consistent way to organizations for creating and deploying applications on a cloud or on-premises environment. This layer fully depends upon the Infrastructure model. It combines that with an abstracted set of middleware services to provide customers with a platform that allows them to develop, run, and manage applications with zero complexity of building or maintaining the infrastructure.

Q2- Explain the Hybrid, Public, and Private Cloud. Also, Compare Them in Terms of Safety.

Ans:- There are three major deployment models of the cloud, which include Public, Private, and Hybrid Cloud. Let’s explore each cloud type one by one!

Public cloud: Public Clouds are nothing but the set of hardware, software, networking, storage, services, applications, and other auxiliary infrastructure owned and operated by third-party providers for the use of individuals or other companies. To ensure data security, third-party providers hide the details of the underlying infrastructure with the help of highly scalable data centers. These clouds are open for usage and deployment by any person. We can share the same hardware with multiple partners and access the services via web browsers. Public clouds are the best choice when the volume of data is high and demands fluctuate. Examples:- Google, Amazon, and Microsoft Azure.

Private cloud: Private Clouds are nothing but the set of hardware, software, networking, storage, services, applications, and other auxiliary infrastructure owned and operated by a firm exclusively for the use of its customers, partners, or employees. A private cloud is solely used by a single organization on its own private network. Most IT industries use private clouds to ensure their organization’s and customers’ privacy and keep their networks away from public clouds. The private cloud is not open for public consumption; that’s why its usage in government organizations is expected. Private clouds are the best choice when the usage patterns are predictable, and users are predefined.

Hybrid cloud: Private Clouds are nothing but the combination of public clouds and private clouds. A hybrid cloud environment combines on-premises computers, storage, and data to increase cloud demand. This cloud combines the features and functionalities of both clouds and applies the most robust approach to implement cloud architecture. A hybrid cloud allows industries to create their separate cloud and permits them as well to pass the control over to any other organization or individual.

Whenever we choose any technology, the major concern of any organization is the safety/security of their data. Let’s compare!

The Benefits of Hybrid Cloud Computing
  • Public cloud:- These clouds have a huge volume of customers and numerous access points so they have a wider variety of security threats. To enable data safety, it usually splits the security responsibilities and assigns the workload security to the tenant and infrastructural security to the provider.
  • Private Clouds:- Private clouds are comparatively more secure than public clouds as they run behind the user’s firewall, but it only depends on an organization’s internal security.

  • Hybrid Clouds:- Hybrid clouds are the most secure ones as they combine the best features of both clouds. It is considered safest because users and admins can reduce data exposure by transferring workloads and data across environments depending upon the security requirements, compliance, policy, or audit.

Ans:- The usage of Cloud computing has been increasing gradually as it is improving the ultimate performance of businesses. With the increase in demand for cloud technology, the number of cloud service provider and platforms are also increasing drastically. Some of the well-known players are Amazon Web Services(AWS), Windows Azure, IBM Cloud, Oracle Cloud, etc.

Sometimes it becomes difficult to choose a suitable cloud computing service for your business as numerous providers compete in the marking. The only thing you need to focus on while choosing a computing service is what you need the service to do. When you are crystal clear with your business model, you can choose the best service that matches your requirements’ features and practical requirements. Below are the majorly used large cloud providers:

  • Hostinger
  • Google BigTable
  • Alibaba Cloud
  • Amazon SimpleDB
  • Cloud-based SQL

Q4:- Describe the Security Aspects of Cloud Computing.

Ans:- Cloud is the most trending technology, enhancing the platform quality and improving the data delivery speed. But with the movement of data from one platform to another, the major concern of every giant is security. Multiple security levels must be considered while talking about cloud environments:

  • Identity management: This level of security ensures that authorized users must use the hardware component or application service.
  • Access Control: This level provides the application services permission to customers so that firms have control and access over other users connected with the same cloud environment. A user must have the authorization to manage the access of another user joining the environment.
  • Authorization and Authentication: It only permits people authenticated and authorized to change or access the application and data.

Q5:- Explain the Transition From Traditional On-premise Computing to Cloud Computing.

Ans:- Cloud Computing proves a big transition from traditional On-premise Computing for many industries that consider their data as a critical element. Cloud computing aims to enhance the ultimate performance of businesses in various aspects, including cost, performance, security, speed, and productivity. Let’s discuss the major reasons for this transition!

Cost:- Cost is considered one of the most debatable elements when we compare cloud with traditional computing. Usually, people consider the cloud as a costly technology but there are scenarios where the cloud is proven as a cheaper one. When the project demands supercomputers, numerous CPUs, etc., cloud computing shows its value by eliminating the cost of buying software, hardware, racks of servers, and the arrangement of data centers.

Cloud computing eliminates the need for an IT team for infrastructure management and reduces the electricity consumption for power and cooling.

Speed:- Cloud computing serves data much faster than traditional computing as it does not have to search multiple data centers and requires no hardware shifting. It also offers on-demand and self-services, which allows the organizations to work accordingly. It provides great flexibility to organizations by delivering their humorous data in minutes/seconds, increasing their productivity.

Productivity:- When we talk about productivity, the first thing that hits our mind is maintenance. Traditional computing requires regular system health checks and upgrades according to business requirements. This process of updating takes a lot of time and even we lost all the previously configured settings after a successful upgradation. But when we use cloud computing, we don’t have to bother about the upgradation or maintenance as everything is automated here. This automation helps IT hubs to meet their business goals and increase productivity.

Performance: System performance is always critical as it depends upon the hardware, software, and servers. Unlike on-premise computing, cloud computing never compromises the system performance as the upgrades are less-expensive here. At the cloud end, we can use the GPUs whenever required, and we only have to pay for the time of use. This stops organizations from poor performance and frequent interruptions.

Conclusion

This blog covers some of the frequently asked Cloud Computing interview questions that could be asked in data science interviews. Using these interview questions as a reference, you can better understand the concept of the cloud and start formulating effective answers for upcoming interviews. The major key takeaways from the discussed cloud computing questions and answers are:-

  1. Cloud computing is an e-computing paradigm that offers automation and standardization to make computing resources easier.
  2. We discussed the three different cloud delivery models, including SaaS, IaaS, and PaaS, and also discussed when to use which model.
  3. We understand the public, private, and hybrid clouds with their safety measures.
  4. We have also discussed some popular cloud service providers, and I can suggest Amazon web’s cloud service for the usage of any project, as it offers different types of cloud computing that can fit your business easily.
  5. At last, we discussed some common reasons for transitioning from on-premise computing to the cloud.

The media shown in this article is not owned by Analytics Vidhya and is used at the Author’s discretion.

Shikha
Shikha

I am a tech enthusiast, a student, and a learner. I am a critical reader and a lover of words who finds writing blogs interesting. I possess the capability to research and learn new technologies quickly.

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The article should have more details so that it could be value-added. Go beyond the commonly available questions instead go with scenario base. Really help for the readers

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