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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

Top 10 Azure Data Services Interview Questions in 2025

chaitanya
chaitanya Last Updated : 13 Dec, 2024
7 min read

Introduction

In today’s world, data is growing exponentially with time with digitalization. Organizations are using various cloud platforms like Azure, GCP, etc., to store and analyze this data to get valuable business insights from it. You will study top 11 azure interview questions in this article which will discuss different data services like Azure Cosmos DB, Azure SQL Database, Azure Data Lake Storage, etc., for storing structured, unstructured, or semi-structured data. Let’s take a look at the below azure interview questions.

Azure Data Services | azure interview questions | azure sql database

Learning Objectives

In this article, we will learn about the below azure interview questions:

  1. Scope of Azure Data Services job profiles
  2. Important Cosmos DB concepts
  3. Cosmos DB offers different database APIs
  4. Understand deployment models provided by Azure SQL Database
  5. Learn about lifecycle policy rules in Azure Blob Storage
  6. Gain knowledge about Azure Storage Data services

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

Table of Contents

Q1. What are Request Units in Cosmos DB?

The request unit is a performance currency that abstracts the system resources required to perform the database operations like read, insert, update, etc., supported by Azure Cosmos DB. Consumed Request Units get charged depending upon the Azure Cosmos DB account creation mode.

Azure Data Services

Q2. What is Time to Live in Cosmos DB?

Time to Live (TTL) in Cosmos DB allows automatically deleting items inside a container after a certain time period by consuming left-over Request Units. Example of configuring TTL in existing Cosmos DB Container:

Select Container-> in settings scroll to Time to Live-> select On and specify TTL value in seconds-> Save.

Azure Data Services | azure interview questions

Q3. What are the Different Database APIs Offered by Cosmos DB? Elaborate with Examples.

Azure Cosmos DB offers various database APIs like MongoDB, Cassandra, Gremlin, NoSQL, and Table. Azure Cosmos DB API for NoSQL provides the option to query items using SQL syntax and offers performance isolation and analytical support. Azure Cosmos DB API for MongoDB provides multiple write locations, automatic shard management, and stores data in document structure using BSON format. Azure Cosmos DB for PostgreSQL is used for storing data in PostgreSQL. Azure Cosmos DB API for Cassandra supports horizontal scaling to store extensive data using column-oriented schema. Example of creating a database school using Azure Cosmos DB API for NoSQL in .NET:

Database database1 = await client.CreateDatabaseAsync(
       id: "school"

)

For example, insert a single document in the collection named student using Azure Cosmos DB API for MongoDB in JavaScript:

db.student.insertOne({

  name:"Chaitanya Shah",

  age: 23,

  address: "24, Wall Colony"

});

Q4. Describe the Purchasing Models Available in Azure SQL Database.

Depending on the deployment model of Azure SQL Database, below are the two purchasing models available:

a. vCore Purchasing Model: The vCore purchasing model allows the users to choose hardware physical characteristics based on their application needs. In this model, customers can independently choose to scale storage, compute resources, etc.

b. DTU-based Purchasing Model: Database Transaction Unit (DTU)–based purchase model provides customers service tiers that are differentiated based on the fixed compute size, storage, read-write rates, and retention period for back-ups.

"Azure Data Services | azure sql database

Q5. What are the Different Deployment Models Provided by Azure SQL Database?

Below are the two deployment models provided by Azure SQL Database:

a. Single Database: Single database type deployment model creates a database with a dedicated database engine, its own set of resources, performance monitoring, and service tiers.

b. Elastic Pool: Elastic pool type deployment model enables the customers to purchase resources for a pool shared by multiple databases. We can add or remove databases from the pool based on resource utilization. An elastic pool solves the problem of resource overprovisioning and under-provisioning.

"Azure Data Services | azure sql database | azure cosmos db

Q6. Scenario-based Question on Azure Data Lake.

While working on project ABC, you created an Azure Data Lake Storage Gen2 account abc_account for storing application and infrastructure logs. The designated retention period for storing application and infrastructure logs is 360 days and 60 days, respectively. As per the current expectations, the logs will not be accessed during the retention periods. Design a solution for the abc_account that will minimize storage costs and automatically delete the logs at the end of each retention period.

Use the archive access tier to store application logs and the cool access tier to store infrastructure logs to minimize the storage costs while storing logs in abc_account. For automatically deleting the logs at the end of each retention period, use Azure Blob storage lifecycle management rules.

Q7. What are the Azure Storage Data Services?

Azure Storage Service provides highly scalable, accessible, secure, and managed services to store objects, blob, create data
lakes, file sharing, etc. Below are the Azure Storage Data services:

  • Azure Blobs: Azure Blob storage allows users to store unstructured data using blobs. Azure Blob storage can be used to store log files, images, documents, data for backup and restore, etc.
  • Azure Files: Azure Files allow users to share files using industry standards such as SMB, NFS, etc. Azure Files can be used to store debugging and development tools needed by VMs.
  • Azure Queues: Azure Queue storage is a messaging service for storing a large number of messages. Azure Queue can be used for asynchronous messaging communication between application components.
  • Azure Tables: Azure Tables allow users to store structured NoSQL data with a schema-less design. Azure Tables can be used to store address books, device information, etc.
  • Azure Disks: Azure Disks are used for storing and accessing data from Azure VMs.
"Azure Storage Service | azure cosmos db

Q8. Scenario-based Question on Azure Blob Storage.

Write the lifecycle policy rule in Azure Blob Storage to transition the block blobs prefixed with container/school or container/college that haven’t been modified in 90 days to the archive tier and blobs not modified over 30 days cool storage tier.

Below is the lifecycle policy rule for the above scenario:

{
  "rules": [
    {
      "name": "agingPolicy",
      "enabled": true,
      "type": "Lifecycle",
      "definition": {
        "filters": {
          "blobTypes": [ "blockBlob"],
          "prefixMatch": [ " container/school ", " container/college " ]
        },
        "actions": {
          "baseBlob": {
            "tierToCool": { "daysAfterModificationGreaterThan": 30 },
            "tierToArchive": { "daysAfterModificationGreaterThan": 90 }
          }
        }
      }
    }
  ]
}

Q9. Write a Query to Create Table Depts in the CompanyDB Azure SQL Database.

The below query will create a table named Depts with the columns DeptNo, DName, and Location:

CREATE TABLE Depts(
DeptNo int Primary Key,
DName nvarchar(50) NOT NULL,
Location nvarchar(50)
);

Here, DeptNo is Primary Key.

azure interview questions | azure sql database | azure cosmos db

Q10. Which Azure Service Should you Choose for Developing an Enterprise Data Lake to Perform Big Data Analytics?

You should use Azure Blob storage to create a data lake for big data analytics. Azure Blob storage allows users to store
unstructured data using blobs. Azure Blob storage provides high security, scalability, data availability, and disaster-recovery capabilities.

Conclusion

Microsoft Azure offers data services like Azure Cosmos DB, Azure SQL Database, Azure Data Lake Storage, etc. for storing structured, unstructured, or semi-structured data. Azure Cosmos DB is a multi-model, fully managed, NoSQL database for modern application development. Azure Storage Service provides highly scalable, accessible, secure, and managed services to store objects, blob, create data lakes, file sharing, etc.

Top companies like Mercedes-Benz, Deloitte, PwC, Accenture, TCS, Razorpay, Swiggy, Uber, etc., are hiring for job profiles related to Azure Data Services skills such as Data Engineer, Data Scientist, R&D-related data roles, etc. at various locations across the world. These job profiles have a wide scope in terms of salary, getting challenging work environments, and solving real-world problems. A working professional requires intensive knowledge of Azure SQL, Azure Data Lake development, developing APIs using Cosmos DB as a database, creating data pipelines using Azure Data Factory, etc., to work in this job.

Below are some important points from the above article on azure interview questions:

  • Azure Cosmos DB offers various database APIs like MongoDB, Cassandra, Gremlin, NoSQL, and Table for connecting to different databases.
  • Using Time to Live (TTL) in Azure Cosmos DB, developers can automatically delete items inside a container.
  • Elastic pools in Azure SQL Database can help organizations use shared resources for Azure SQL.
  • We got an understanding of which Azure Storage Data service should be used based on the scenario and data type.
  • Apart from this, we have also seen the Azure Storage access tiers and lifecycle management policies.

I hope you liked my article on azure interview questions. Share your feedback with me in the comments section.

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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