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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

Commonly Asked Ques During Microsoft Azure Interviews

chaitanya
chaitanya Last Updated : 23 Jun, 2022
5 min read

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

Introduction on Microsoft Azure

Microsoft Azure is a public cloud computing platform. Azure provides different categories of cloud services such as infrastructure as a service (IaaS), platform as a service (PaaS), software as a service (SaaS), and serverless. Organizations across the globe prefer to use Microsoft Azure while developing the solutions as it provides dynamic scalability, disaster recovery, and security.

Therefore, it is very beneficial for developers if they have good knowledge about Azure.

In this article, we will discuss some important questions on Azure that will help you to get well prepared for your interviews.

Below are some important interview questions for Azure:

Interview Questions for Azure

1. How will you differentiate between public and private cloud?

Public Cloud Private Cloud
In the public cloud, in a shared environment data of multiple organizations are stored but kept isolated from one another.  Private Cloud stores the data of a single organization.
It is managed by the cloud service provider and customers use them.  Private Cloud is managed and used by a single organization.
Public Cloud has shared servers and resources.  Private Cloud has dedicated servers and resources.
Security is less in the public cloud as the resources are shared.  Private Cloud is highly secured as it has dedicated resources.

2. What are Azure resources and Azure resource groups?

Every entity created and managed by Azure is known as an Azure resource. Examples of azure resources include Azure Storage Account, Azure SQL, Azure Virtual Machines, etc. Azure resource group holds the related Azure Resources for an Azure solution. Using the Azure resource group, we can easily manage the life cycle of all the azure services which are present inside a resource group together.

What are Azure resources and Azure resource groups

3. What are the different protocols supported by Azure Application Gateway? Is it possible to restore an Application Gateway and its public IP if it has been deleted?

The different protocols supported by the Azure Application gateway are HTTP, WebSocket, HTTPS, and HTTP/2. It is not possible to restore an Application Gateway and its public IP if it has been deleted. When deleted accidentally in any scenario, you will be required to create a new application gateway.

Azure Application Gateway

4. What is the difference between Service Bus Queues and Storage Queues?

Service Bus Queues Storage Queues
Service Bus Queues supports message ordering using First In First Out (FIFO).  Storage Queues do not guarantee message ordering.
Storage Queues support atomic operation.  Storage Queues do not support atomic operation.
It supports batch sending and receiving of messages.  It supports batch receiving of messages but does not support batch sending.
It supports queue-level lease/lock precision.  It supports message-level lease/lock precision.
It supports a lock-based exclusive access mode.  It supports a lease-based exclusive access mode.

5. Consider a scenario in which work as an Azure Administrator for your organization. You have been asked by Team Lead to set the default password for all the new users added to the Active Directory. Is it possible?

Yes, it is possible to set the default password for the first-time user in the azure active directory.

6. What is the difference between Azure Table Storage and the Azure SQL service?

Azure Table Storage Azure SQL
Azure Table Storage stores non-relational structured data.  Azure SQL stores relational data.
Azure Table Storage stores data in key-value format. Here, data is referred to as an entity.  Azure SQL stores data in rows and columns.
It is mostly used for storing diagnostic information or log data.  It is mostly used for transactional data.

7. What is the maximum number of triggers that a single Azure Function can have?

There can be only one trigger present at maximum inside an Azure Function.

8. What is data profiling in Azure?

Data profiling is a feature of the Azure Data Catalog service which examines the data

from supported data sources in the catalog and collects useful information and related statistics about that data. For including a profile of your data assets, choose Include Data Profile in the data source registration tool whenever you register a data asset.

9. How can the issue of high load on the application be resolved when there is no man support on the floor using Azure?

To resolve the above issue, we can use VM Scale sets with proper conditions and configurations to provision a new VM whenever the load to the application increases.

Developers can create and manage a group of VMs which are load balanced using Azure VM Scale Sets. Configure scale sets in such a manner that the count of VMs can automatically be increased or decreased based on a pre-defined schedule or on the application demand. Azure VM Scale sets allow developers to configure, update and large VMs centrally and also ensure high availability of the applications. It is easy to support the development of large-scale applications supporting compute loads, big data, etc. using Azure VM Scale sets.

10. How do you delete a blob with snapshots in Azure?

For deleting a blob with snapshots, you will first have to delete all of its snapshots. If you want to delete a blob and its snapshots both at the same time, you can use the Delete Blob operation for that.

snapshots in Azure

11. How can we secure database connection strings using Azure Key Vault?

Create a secret in Azure Key Vault, give any name to the secret, and provide database connection strings as a secret value. Thus, in the above way, we can secure the database connection strings using a secret in Azure Key Vault.

Azure Key Vault

12. On your workstation, you have created a container image named ContainerX. After that, you have created an Azure web app for containers named Demo that will use container image ContainerX. You need to upload ContainerX to Azure. The solution provided by you must ensure that Demo can use container image ContainerX. To which Azure Service should you upload the created container image ContainerX?

Container image ContainerX should be uploaded to the Azure container registry. After that, configure the registry credentials inside the web app such that the app service to which the web app is deployed is able to pull the image from the Azure container registry. In the Azure portal, in the web app for containers named Demo go to Container settings and update the Image source, Registry, and Save.

Conclusion on Microsoft Azure 

In this article, we have discussed some common questions that can be asked in Azure interviews. However, it’s recommended to read Microsoft’s official documentation for gaining further knowledge about the questions and Azure services discussed here. Below are some major takeaways from the above Microsoft Azure article:

1. We have learned how to secure database connection strings using Azure Key Vault.

2. We have seen how is data profiling, azure resources, and azure resource group.

3. We got an understanding of how an Azure function can have only one trigger at maximum.

4. We have seen how Azure Storage Queues and Azure Service Bus queues are different from each other.

5. Apart from this, we also saw under which scenarios it is beneficial to use Azure VM Scale Sets and Azure Application Gateway.

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

chaitanya
chaitanya

Full Stack developer with 2.6 years of experience in developing applications using
Azure, SQL Server & Power BI. I love to read and write blogs. I am always willing to learn new technologies, easily adaptable to changes, and have a good time
management, problem-solving, logical and communication skills

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