33 Azure Interview Questions: From Basic to Advanced

Intermediate Azure Interview Questions

These questions will test your hands-on experience with Azure services, configurations, and effective usage in various scenarios. With intermediate questions, you are expected to demonstrate a deeper understanding of Azure’s core services. 

Intermediate questions' main objective: Evaluate the candidate’s intermediate and implementation-level understanding of Azure’s concepts.

Key concepts to know

To make sure you are prepared, have a solid understanding of the following concepts:

• Compute solutions: Be familiar with computer services (such as  Azure Kubernetes Service (AKS), Azure Functions, Azure Virtual Machines, and Azure App Services. ) and be able to explain scaling options and how to implement them efficiently.
• Networking: Proficiency in configuring and managing Azure Virtual Networks, Network Security Groups (NSGs), Azure VPN Gateway, Azure Load Balancer, and Azure ExpressRoute for hybrid connectivity.
• Security and compliance: Deep understanding of role-based access control and how Azure ensures compliance with industry standards. Additionally, understand important services like Azure Security Center, Azure Key Vault, and Azure Policy. 
• DevOps practices: Experience with CI/CD pipelines using Azure DevOps, infrastructure automation with ARM templates, and configuration management tools like Terraform.
• Monitoring and management: Use Azure Monitor, Application Insights, and Log Analytics to monitor and manage Azure resources and applications.

Questions you might be asked

Here are some of the samples of questions you might encounter during an intermediate Azure interview, along with suggested answers.

11. What are IAM roles, and how are they used in Azure?

Azure Identity and Access Management (IAM) is a powerful tool for managing users' access to Azure resources. It allows us to define who can access what resources and what actions they can perform. It is based on:

• Identification and authentication: Users in Azure need to be identified and authenticated via Microsoft Entra ID.
• Roles and permissions: Once authenticated, users are assigned roles that define their permissions. Some default roles are Owner, Contributor, and Reader. However, you can customize and create specific roles, defining what actions can be performed. 

12. How does Azure Load Balancer work, and what types are available?

Azure Load Balancer is a service that distributes incoming network traffic across multiple backend resources to ensure high availability and reliability. It supports automatic failover and load distribution across virtual machines or instances. 

There is a paid version (Standard) and a free one (Basic). Both of them can be deployed as: 

• Public load balancer: Distributes incoming internet traffic to virtual machines or instances in the backend pool and provides outbound connectivity for VMs.
• Internal load balancer: Balances traffic within a virtual network, enabling applications to communicate efficiently within a private or hybrid environment.

The Load Balancer operates at Layer 4, handling TCP and UDP traffic. It performs fundamental load-balancing tasks by directing incoming traffic from its front end to backend pool instances through configured load-balancing rules and health probes. 

These backend pool instances can consist of Azure Virtual Machines or Virtual Machine Scale Sets.

13. What is Azure Virtual Machine, and how is it different from Azure App Services?

Azure Virtual Machines are virtualized server instances (for Windows or Linux) in the cloud. You have full control of the OS and the apps. 

On the other hand, Azure App Services is a streamlined service for web apps and APIs, so you don't have to worry about the underlying structure.  

While Virtual Machines give you a deep level of control, App services are great when you want to focus more on the code and less on the setup.

14. What are the main differences between Blob Storage and Azure Files? What scenarios are they usually used for?

The main differences between both services are: 

• Data types: While Blob Storage is designed for unstructured data such as images or documents, Azure File is optimized for structured data that needs to be shared across multiple machines. 
• Access protocol: Blob Storage is accessed using Accessed via HTTP/HTTPS. The Azure Files are accessed using the Server Message Block (SMB) protocol.

Both services have properties optimized for dealing with different data types, so they are used for different scenarios. 

Azure Blob Storage is ideal for storing large amounts of unstructured data that do not require a traditional file system, such as Blackups, Media Storage, or Content delivery. 

On the other hand, Azure File Storage is ideal for scenarios where files need to be shared across multiple virtual machines or on-premises servers, supporting traditional file system operations like File sharing or Lift and Shift.

To explore Azure services and their main use cases, you can check out DataCamp’s course on Azure Architecture and Services.

15. What are the different cloud deployment models in Azure?

There are three cloud deployment models available in Azure:

Advanced Azure Interview Questions

These questions are designed for senior positions where you must have in-depth knowledge and experience in designing and implementing scalable, robust, and secure Azure solutions. 

Advanced questions’ main objective: Evaluate the candidate’s advanced understanding of Azure’s concepts and services.

Key concepts to know

To be fully prepared, you should have strong expertise in the Azure ecosystem and how to design, build, and deploy applications using its services. This means understanding:

• Architecture and design: Deep understanding of best practices for designing scalable and resilient Azure solutions. 
• Multi-region deployments: Strategies for deploying applications across multiple regions for improved availability, disaster recovery, and understanding best practices to deploy global applications. 
• Load balancing and autoscaling: Implementing Azure Load Balancer, Application Gateway, Azure Front Door, and Autoscaling to ensure application performance and reliability.
• Disaster recovery: Using Azure Site Recovery, Azure Backup, and geo-redundant storage solutions to ensure business continuity.
• Hybrid and multi-cloud solutions: Integrating Azure Stack, Azure Arc, and hybrid identity solutions to enable seamless operations across cloud and on-premises environments.
• Advanced security practices: Implementing Azure Sentinel, Azure Security Center, Azure Policy, and custom security solutions to protect Azure resources and data.

Questions you might get

Here are some of the samples of questions you might be asked during an advanced Azure interview, along with suggested answers.

17. Explain Azure Site Recovery and its role in disaster recovery planning.

Azure Site Recovery (ASR) is a disaster recovery solution that helps businesses ensure business continuity by replicating workloads running on physical and virtual machines to a secondary Azure region or on-premises site. Given an outage or disaster, ASR facilitates automatic failover to the secondary site, minimizing downtime and data loss.

Key features of Azure Site Recovery include:

• Automated replication and recovery: ASR automates the replication process, ensuring that data and applications are consistently backed up to a secondary location.
• Flexible failover options: ASR supports planned and unplanned failovers, allowing businesses to test their disaster recovery plans without impacting production workloads.
• Application consistency: ASR provides application-consistent snapshots, ensuring that multi-tier applications recover consistently after failover.

Azure Site Recovery is a critical component of disaster recovery planning, providing businesses with the tools they need to protect against data loss and ensure rapid recovery in case of an unexpected event.

18. Explain Azure Functions and exemplify the most common use cases together with a service schema. 

Azure Functions is a serverless computing service that allows developers to run code on-demand without managing infrastructure. It automatically scales based on demand and charges only for the execution time used.

Use Cases for Azure Functions:

• Event-driven tasks: Execute tasks in response to events or triggers, such as HTTP requests, messages in a queue, or changes in a database.
• Background processing: Run background processes like data processing, batch jobs, or file uploads.
• Real-time processing: Handle real-time stream and event processing from IoT devices or cloud applications.
• Machine learning and AI: Integrate with machine learning models and AI services for tasks like text completion, image classification, and semantic search.
• Scheduled tasks: Run code based on a defined schedule, such as cleaning up databases or sending periodic notifications.
• Web APIs: Build scalable web APIs with HTTP-triggered functions that can connect to other services or act as webhooks.
• Serverless workflows: Serve as the compute component in serverless workflows, integrating with services like Azure Logic Apps.
• Reliable messaging: Create advanced event-driven messaging solutions using Azure messaging services like Storage queues and Service Bus.

Azure Functions is versatile and integrates seamlessly with other Azure services, making it ideal for various applications. A good example would be using it within a process file upload pipeline. 

Let’s take a retail data processing solution as an example: A partner system can submit product catalog information as files to blob storage. As the files are uploaded, a blob-triggered function can validate, transform, and process them in the main system.

Azure Functions application architecture diagram. Image by Microsoft. 

You can get more information about all use cases in the official Microsoft Documentation.

19. How do you generate a basic web application using Azure?

To run a web application using Azure, we could use the following services: 

• Microsoft Entra ID provides a single identity control plane to manage permissions and roles for web application users. It integrates with App Service and simplifies authentication and authorization for web apps.
• App Service for building, deploying, and scaling the web application.
• Azure Monitor will collect, analyze, and act on telemetry data across your deployment.
• Azure SQL Database to store the associated data. 

The idea of the application would be: 

1. A user issues an HTTPS request to the App Service's default domain on azurewebsites.net. This domain automatically points to your App Service's built-in public IP. 
2. Easy Auth, a feature of Azure App Service, ensures that the user accessing the site is authenticated with Microsoft Entra ID.
3. Your application code is deployed to the App Service, which handles the request. For example, that code can connect directly to an Azure SQL Database instance using a connection string configured as an app setting in the App Service.
4. Application Insights logs the information about the original App Service request and the Azure SQL Database call.

This would be the corresponding architecture diagram: 

Azure basic web application architecture diagram. Image by Microsoft. 

20. What steps would you consider to create a multi-region Azure deployment for a global application?

Designing a multi-region Azure deployment requires careful planning and several key strategies:

1. Architecture design:
• Deploy in at least two regions (primary and secondary).
• Use Azure Front Door for global load balancing and failover.
2. Data replication:
• Use active geo-replication and auto-failover groups for SQL Database.
• Implement geo-replication for Azure Cosmos DB.
• Utilize read-access geo-redundant storage (RA-GRS) for Azure Storage.
3. Application services:
• Deploy web apps and APIs as separate App Service apps in both regions.
• Use Azure Function Apps for background tasks.
4. Networking and security:
• Manage DNS with Azure DNS.
• Secure traffic through Azure Front Door and its WAF.
5. Monitoring and health probes:
• Configure health probes in Azure Front Door.
• Use Azure Monitor and Application Insights for performance tracking.
6. Cost optimization and performance:
• Implement caching with Azure Cache for Redis and Azure CDN.
• Design for scalability and use sharding for SQL databases.
7. Operational excellence:
• Organize resources into separate resource groups.
• Regularly test failover procedures and update disaster recovery plans.

This approach ensures high availability, security, and performance for your global application.

21. What strategies would you use for load balancing in Azure?

Azure offers several load-balancing options to distribute traffic efficiently and maintain application availability:

The choice of load-balancing strategy depends on the application's specific needs, such as traffic patterns, security requirements, and geographic distribution.

22. How do you ensure compliance and governance across Azure environments?

Ensuring compliance and governance in Azure involves implementing policies and best practices to manage resources and enforce standards. These are some of the Azure services that can help implement a governance model.

1. Azure Policy: Defines and enforces rules across Azure resources. This tool allows organizations to apply governance standards, such as naming conventions, resource configurations, and allowed resource types.
2. Azure Blueprints: Azure Blueprints can automate the deployment of compliant environments. Blueprints provide templates for deploying resources that adhere to organizational policies and regulatory requirements.
3. Role-Based Access Control (RBAC): Implement RBAC to manage access to Azure resources. RBAC allows organizations to assign specific roles and permissions to users, ensuring that only authorized personnel can access sensitive resources.
4. Security Center and Compliance Manager: Utilize Azure Security Center and Compliance Manager to continuously monitor and assess compliance with industry standards, such as GDPR, HIPAA, and ISO 27001.

By leveraging these tools and practices, organizations can maintain control over their Azure environments and ensure compliance with internal policies and external regulations. 

DataCamp’s Azure Management and Governance course can teach you more about governance.

23. What are Managed Identities, and why should you use them?

Managed Identities provide an automatically managed identity in Microsoft Entra ID for Azure resources (like VMs, App Services, or Functions).

• The problem it solves: It eliminates the need for developers to manage credentials (like passwords or connection strings) in their code.
• How it works: The Azure resource uses this identity to authenticate to services like Azure Key Vault or SQL Database securely, without you ever seeing or rotating a password.

Azure Data Architect Interview Questions

Let’s review some questions you may encounter if you apply for a data architect role with Azure.

24. What is Integration Runtime? What are the different types of integration runtimes?

Integration Runtime is a computational infrastructure that Azure Data Factory uses to deliver integration features, such as Data Flows and Data Movement, across various network settings.

There are three different types of integration runtimes:

25. What Azure feature may be used to prevent high application load in the case of no-man assistance on the flow?

To prevent a high application load without human intervention, you can always use Azure Autoscale. Autoscale allows you to automatically scale your applications or resources based on demand and predefined rules and metrics, such as CPU usage or memory usage. This provision provides enough resources to support the demand on your application without over-provisioning and incurring unnecessary costs.

Additionally, Azure Traffic Manager can distribute traffic across multiple regions, helping to balance the load and improve application performance and availability. 

26. How does Azure Traffic Manager differ from Azure Load Balancer?

Azure Traffic Manager is mainly responsible for sending traffic globally based on routing methods such as geographic location, performance, or priority. This leads to an enhanced user experience that reflects how your application is distributed worldwide. 

Your public endpoints will also experience benefits such as high accessibility and responsiveness. For diverse application objectives and spontaneous recovery systems, Traffic Manager offers a variety of traffic-routing mechanisms and endpoint-tracking solutions. 

On the other hand, Azure Load Balancer handles traffic routing within a certain region. It distributes incoming network traffic across multiple virtual machines or services within that region, ensuring high performance and availability. 

It offers a high-performance, low-bandwidth Layer 4 load-balancing solution for all UDP and TCP protocols. It can ensure the smooth handling of millions of queries per second while maintaining high availability.

27. Explain the Service Bus Queue and Storage Queue.

Azure Service Bus Queues belong to the Azure messaging framework and include queuing, publishing, and subscribing, among other things. They also include built-in dead-letter queues for handling message failures and allow you to set message expiration times. 

Service Bus Queues are ideal for connecting disparate application components using communication protocols, data contracts, trust domains, or security protocols.

Azure Storage Queues belong to the Azure storage framework and are known for their simplicity and ease of use. They allow easy debugging by using the local Azure Storage Emulator. 

The Azure Storage Queue tools enable you to quickly review the top 32 messages and visualize the contents of those belonging to XML/JSON right from Visual Studio. 

Another feature of storage queues that ensures smooth development and QA operations is that their contents can be emptied when needed. 

28. What is the difference between a Data Lake and a Data Warehouse in Azure?

Azure Data Lake (ADLS Gen2) is designed to store massive amounts of unstructured, semi-structured, and structured data (Schema-on-Read). Azure Synapse Analytics (Data Warehouse) is optimized for relational, structured data and high-performance SQL querying (Schema-on-Write).

Azure Data Scientist Interview Questions

Another role that requires Azure knowledge is that of a data scientist. Let’s review some of the Azure questions you may be asked if you’re applying for this role.

29. How many types of datasets are supported in Azure Data Factory?

The datasets supported in Azure Data Factory are the following: 

• CSV
• Excel
• Binary
• Avro
• JSON
• ORC
• XML
• Parquet

30. How do you train and deploy a machine learning model in Azure Machine Learning Studio?

Azure Machine Learning provides three main interfaces for building models, catering to different skill levels and requirements:

1. Automated ML (AutoML): Best for rapid prototyping. You upload data, and Azure automatically iterates through various algorithms and hyperparameters to find the best model for your specific metric.
2. Azure ML Designer: A drag-and-drop interface where you visually connect datasets and modules to create a training pipeline without writing code.
3. Notebooks & Python SDK (Code-First): The standard for professional data scientists. You write Python code (using the Azure ML SDK v2) within Jupyter Notebooks hosted in Azure Studio to train, register, and manage models.

The General Workflow:

1. Prepare Data: Register datasets in the Azure ML Data Store.
2. Compute: Create a Compute Cluster (managed VMs) to run the training script.
3. Train: Submit a "Job" (experiment) that runs your training script on the compute cluster.
4. Register: detailed metrics and the final model file (pkl/onnx) are saved in the Model Registry.
5. Deploy: The model is containerized (Docker) and deployed to an Online Endpoint (for real-time inference) or a Batch Endpoint (for large-scale batch processing).

31. How can you make a SQL Azure Database perform better?

To enhance the performance of an SQL Azure Database, consider the following strategies:

1. Query optimization:
• Execution plans and statistics: Analyze query execution plans and update statistics to identify and optimize slow-running queries.
• Indexes: Create and maintain appropriate indexes to speed up data retrieval.
2. Dynamic Management Views (DMVs):
• Use SQL Azure's DMVs to monitor performance metrics, identify bottlenecks, and manage resources effectively.
3. Network considerations:
• Latency and bandwidth: To minimize network latency and maximize bandwidth, place your database close to your application in the same Azure region.
• Connection pooling: Implement connection pooling to reduce the overhead of establishing connections.
4. Scaling:
• Vertical scaling: Increase the performance level of your SQL Azure Database by choosing a higher service tier.
• Horizontal scaling: Sharding or partitioning to distribute the load across multiple databases.
5. Caching:
• Implement caching strategies, such as using Azure Cache for Redis, to reduce the load on the database by storing frequently accessed data in memory.
6. Performance monitoring tools:
• To continuously monitor and optimize performance, utilize Azure SQL Database's built-in performance monitoring and tuning tools, such as Query Performance Insight and Automatic Tuning.

By implementing these strategies, you can significantly improve the performance of your SQL Azure Database.

32. How is the performance of pipelines optimized in Azure Data Factory?

Optimizing the performance of Azure Data Factory pipelines involves enhancing data movement, transformation, and overall pipeline execution. 

Here are some optimization strategies:

1. Integration runtime selection: Choose the appropriate integration runtime based on the location of your data source and destination. This ensures that compute resources are closer to the data, reducing latency and improving performance.
2. Parallel processing: Break data into smaller chunks and execute them in parallel within pipelines or data flow activities. This parallelism can significantly speed up data processing.
3. Efficient data flows: Minimize unnecessary transformations and data shuffling in your mapping data flows. Reducing these operations can streamline the data flow and enhance performance.

By implementing these strategies, you can effectively optimize the performance of your Azure Data Factory pipelines.

33. How do you manage and store data in Azure, and what tools do you use?

Azure provides various services for managing and storing data in the cloud:

• Azure SQL Database: A fully managed relational database service provides high availability, automatic backups, and scalability. It supports various SQL Server features and can store and manage structured data.
• Azure Cosmos DB: A globally distributed, multi-model database service that supports NoSQL databases such as document, key-value, graph, and column-family databases. It offers automatic scalability and high availability and can store and manage unstructured data.
• Azure Blob Storage: A fully managed object storage service for storing and managing unstructured data such as images, videos, and documents. It provides high availability, durability, and scalability, and can be accessed using REST APIs.
• Azure Data Lake Storage Gen 2: A scalable and secure data lake service for storing and managing large amounts of unstructured and structured data. It provides granular access controls and can be accessed using various tools, such as Azure Data Factory and Azure Databricks.
• Azure Backup: A backup and disaster recovery service for protecting and recovering data in Azure. It provides automatic backups and can be used to backup data from on-premises environments and Azure services such as Azure VMs and Azure File Shares.
• Azure Site Recovery: A disaster recovery service for replicating and recovering applications and workloads to Azure or another location. It provides near-zero RPO and RTO and can replicate workloads from on-premises environments and Azure services such as Azure VMs.

Conclusion

In this article, I presented 30 targeted questions to help you prepare for your upcoming interview. Organized into categories—Basic, Intermediate, and Advanced Azure Questions, as well as Data Architect and Data Scientist-Specific Questions—these will equip you to showcase your expertise!

If you want to improve your Azure and data practitioner skills, here are some resources you could check out:

• Introduction to Azure 
• Microsoft Azure Fundamentals (AZ-900)
• Azure Architecture and Services 
• Introduction to Data Engineering
• Understanding Cloud Computing

Good luck with your next interview!