Interview Prep
Career
GenAI
Prompt Engg
ChatGPT
LLM
Langchain
RAG
AI Agents
Machine Learning
Deep Learning
GenAI Tools
LLMOps
Python
NLP
SQL
AIML Projects

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

Frequently Asked Microsoft Azure Interview Questions

Prashant
Prashant Last Updated : 08 Aug, 2022
6 min read

Introduction

Microsoft Azure is an expanding suite of cloud computing services developed by Microsoft that hosts your existing apps, streamlines the creation of a new application, and improves our on-premises applications. It is an online portal that enables you to access and manage Microsoft cloud services and resources. With these services and resources, your data can be stored and changed based on your needs.

Microsoft Azure
(Source:saviantconsulting.com)

The Azure platform intends to assist organizations in overcoming obstacles and achieving their organizational objectives. It is compatible with open-source technology and supports all industries, including e-commerce, banking, and a range of Fortune 500 firms. This allows users the freedom to utilize their preferred tools and technologies. Azure also offers four distinct cloud computing models: infrastructure as a service (IaaS), platform as a service (PaaS), software as a service (SaaS), and serverless.

How does Microsoft Azure Work?

It is vital to understand the fundamental workings of Azure so that we may build our applications on Azure successfully with high availability, data residency, resilience, etc.

Microsoft Azure is built entirely on the notion of virtualization. Consequently, similar to other virtualized data centers, racks are also included. Each rack has its power supply and network switch and is also equipped with the Fabric-Controller software. Fabric-controller is a distributed program responsible for controlling and monitoring rack-mounted servers. If a server fails, the Fabric controller detects and restores it. Each of these Fabric-Controllers is connected to a software application named Orchestrator. This Orchestrator consists of web services and a Rest API for creating, updating, and deleting resources. When a user submits a request using either PowerShell or the Azure portal. First, it will be sent to the Orchestrator, where it will perform three basic actions:

  • Authenticate the User

  • It will authorize or verify if the user is permitted to perform the desired task.

  • It will check the database for space availability based on the resources and then forward the request to a suitable Azure Fabric controller for execution.

Why use Microsoft Azure?

Flexibility

Azure is inherently adaptable as a cloud-based solution; it can back up your data in nearly any language, operating system, and anywhere. Additionally, you determine the frequency and scope of your backup schedule (daily, weekly, monthly, etc.).

Application development and hosting

Azure supports the creation of any web application. After testing is complete, Azure may assist with hosting the application.

Recovery 

Occasionally, the inconceivable becomes the present reality. Microsoft Azure’s high-speed, geographically dispersed architecture provides endless alternatives for disaster recovery strategies.

Economical

Microsoft Azure offers a pay-as-you-go pricing model that enables businesses to effectively manage their budgets by charging them only for the services they utilize during each billing cycle.

Interview Questions.

1. What deployment environments does Azure offer?

Azure offers two environments for deployment:

Staging Environment:

  • It provides a platform for validating your application’s modifications before it is live in the production environment.

  • At this point, the app may be recognized using the Globally Unique Identifier (GUID) in URL form provided by Azure (GUID.cloudapp.net)

Production Environment:

  • This environment contains active applications.

  • It may be distinguished from the staging environment through a DNS-friendly URL (servicename.cloudapp.net).

2. Define IaaS, PaaS, and SaaS.

 

Microsoft Azure
(Source:stackscale.com)
  • IaaS abbreviates Infrastructure as a Service. It provides various infrastructure-level features, such as an operating system and network connection. It adheres to a pay-per-use model. It serves as an application host. Azure VM and VNET are examples of IaaS.
  • PaaS abbreviates Platform as a Service. It consists mostly of infrastructure abstraction that enables speedier application development without the bother of hosting administration. PaaS includes Azure online applications, cloud services, storage services, etc.
  • SaaS abbreviates for Software as a Service. These apps are offered via a service delivery approach in which an organization just utilizes applications. Subscription-based charges or advertisements follow SaaS. SaaS examples include Gmail, Office 365, SharePoint Online, and others.

3. What is Azure Blob Storage?

Azure Blob (binary huge object) storage is the cloud’s object storage service. It can store vast amounts of unstructured data in text or binary format and is appropriate for sending documents, media, or text straight to browsers. From any geographical location, data can be accessed. Blobs are organized into containers and associated with user accounts. This service has three elements:

  • Storage account: This can be either a standard storage account or a blob storage account registered in Microsoft Azure.

  • Container: The purpose of containers is to group blobs. Each container has a limitless capacity for blob storage. The container’s name should be in lowercase.

  • Blob: A blob is any type and size of file or document. Azure supports the following sorts of blobs:

  • Block blobs: Text and binary files up to 195GB, 50,000 maximum 4 MB blocks

  • Append blobs: Adds operations such as data logging to log files.

  • Page Blob: For frequent read or write operations.

4. What are the benefits of Azure Scaling?

Autoscaling is a feature of Azure that allows automatic scaling. Autoscaling aids in the management of fluctuating demand in Cloud Services, Mobile Services, Virtual Machines, and Websites. Some benefits are listed below:

  • Enhances application performance

  • Depending on demand, scale up or down.

  • Scaling the schedule to certain periods

  • Extremely cost-effective

5. Explain Azure Fabric.

Azure Fabric is the primary fundamental notion. It provides the Azure Fabric Controller service. It is known as the Azure operating system because it manages/oversees:

  • All functions (processing) and assets

  • Delivery and activation of services

  • Monitoring the condition of every service

  • Allocation and release of resources

  • Provisioning virtual machines, terminating, etc.

  • Patches for the installed OS are updated in the most automatic manner on VM.

6. Explain Azure Backup. Mention some of its merits.

Microsoft’s Azure Backup is a cloud-based solution for backing up Azure Windows VMs, Azure Managed Disks, Azure File shares, SQL Server databases, SAP HANA databases, Azure PostgreSQL databases, and more.Among the key advantages of Azure Backup are:

  • Azure Backup provides a practical method for backing up on-premises data to the cloud. Without constructing significant on-premises backup systems, you may have a backup for both the short and long term.

  • Azure Backup offers independent backups to safeguard original data from unanticipated harm. The backups are kept in a Recovery Services storage with built-in recovery point management.

  • Azure Backup provides high availability with minimal maintenance and monitoring costs by exploiting the cloud’s basic capabilities and endless storage space.

7. What is the table notion in Windows Azure?

A table is a kind of Azure Storage where you may store data. BLOBs are placed in a compartment, and entities are placed on a table. The following are the central ideas in a table:

  • Tables enable the storing of structured data.

  • A storage account can include from 0 to n tables.

  • Tables hold data as a collection of elements.

  • An element has an essential key and attributes in the form of a key-value pair.

8. What are the two kinds of Azure Web Service roles?

A cloud service role is a collection of managed and load-balanced virtual machines collaborating to complete tasks. There are two varieties of Azure Web Service roles:

  • Web Roles:

It is a cloud service role used to execute web applications developed in programming languages supported by IIS (Internet Information Services) like ASP.NET, PHP, etc. It deploys and hosts programs automatically via the user IIS

  • Worker Role:

It executes apps and other operations that are not dependent on IIS. It conducts background operations in addition to web roles. It does not require IIS and operates user programs independently.

9. What are the advantages of Azure Queue Storage?

Queue storage facilitates message queueing for huge workloads in a simple, cost-effective, and durable manner. Some of its benefits are:

  • It provides extensive client libraries for Java, Android, C++, PHP, Ruby, and others.

  • It enables users to create adaptable applications and distinct features for improved durability.

  • It guarantees that user applications are scalable and less susceptible to component failure.

  • It permits the monitoring of queues to ensure abrupt traffic spikes do not overload servers.

10. How has hybrid cloud integration benefited Azure?

The Hybrid Cloud increases productivity by using Azure and the Azure stack to develop and deploy cloud-based and on-premises applications. The following are some of the ways that Azure has profited from hybrid cloud integration:

  • It achieves increased productivity by combining Azure services and DevOps procedures and technologies.

  • Users can utilize continuously updated Azure services and other Azure Marketplace apps.

  • It enables deployment regardless of location, whether in the cloud or on-premises.

  • This permits the creation of applications at a faster rate.

Conclusion

This article provides information on one of Microsoft’s cloud computing services, Azure. Microsoft Azure has proven to be the cloud platform with the quickest growth due to its more than 200 service offerings and pay-per-use pricing model. Microsoft Azure’s income has steadily increased from $880 million in 2015 to a staggering $46.2 billion in 2022.

This great expansion in Azure has opened the way for several enterprises by offering numerous chances in both technical and non-technical fields, consequently making it a very profitable field in which to establish a career.

We hope that these interview questions will help you prepare for forthcoming interviews. However, if you have any queries regarding Microsoft Azure Interview Questions, please feel free to ask. Below is a list of the key points:

  • Azure’s several cloud computing paradigms, namely IaaS, PaaS, SaaS, and serverless.

  • Azure features such as Azure Fabric, Azure Backup, and others.

  • Learn that the Web Role is supported by IIS, whereas the worker role is independent of IIS.

  • Why use Azure and many other topics?

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

blobblogathonIaaSinterview questionsMicrosoft azure
Prashant
Prashant

Hello, my name is Prashant, and I'm currently pursuing my Bachelor of Technology (B.Tech) degree. I'm in my 3rd year of study, specializing in machine learning, and attending VIT University.

In addition to my academic pursuits, I enjoy traveling, blogging, and sports. I'm also a member of the sports club. I'm constantly looking for opportunities to learn and grow both inside and outside the classroom, and I'm excited about the possibilities that my B.Tech degree can offer me in terms of future career prospects.

Thank you for taking the time to get to know me, and I look forward to engaging with you further!

Free Courses

Generative AI 4.7

Generative AI - A Way of Life

Explore Generative AI for beginners: create text and images, use top AI tools, learn practical skills, and ethics.
Large Language Models 4.5

Getting Started with Large Language Models

Master Large Language Models (LLMs) with this course, offering clear guidance in NLP and model training made simple.
Prompt Engineering 4.6

Building LLM Applications using Prompt Engineering

This free course guides you on building LLM apps, mastering prompt engineering, and developing chatbots with enterprise data.
LlamaIndex 4.8

Improving Real World RAG Systems: Key Challenges & Practical Solutions

Explore practical solutions, advanced retrieval strategies, and agentic RAG systems to improve context, relevance, and accuracy in AI-driven applications.
RAG systems using LlamaIndex 4.7

Microsoft Excel: Formulas & Functions

Master MS Excel for data analysis with key formulas, functions, and LookUp tools in this comprehensive course.

Recommended Articles

Responses From Readers

Clear

Write for us

Write, captivate, and earn accolades and rewards for your work
  • Reach a Global Audience
  • Get Expert Feedback
  • Build Your Brand & Audience
  • Cash In on Your Knowledge
  • Join a Thriving Community
  • Level Up Your Data Science Game
imag

We use cookies essential for this site to function well. Please click to help us improve its usefulness with additional cookies. Learn about our use of cookies in our Privacy Policy & Cookies Policy.

Show details