Important Azure interview questions and answers for 2023

Azure is a cloud computing platform managed by Microsoft. It offers services and tools for building, deploying, and managing applications and services in the cloud. The Azure services can be accessed through the internet.

These include virtual machines, databases, storage, and networking, among others. Users can select the services they need, configure them according to their requirements, and deploy their applications on the Azure platform.

Azure is one of the most widely used cloud computing platforms in the world with millions of active customers in over 190 countries. They range from startups and small businesses to large enterprises and government agencies.

There are numerous Azure services across various categories. Here, we will provide a brief overview of some key services in each category:

Compute:

Azure Virtual Machines: Infrastructure as a Service (IaaS) solution, providing flexible, on-demand VMs. Azure Functions: Serverless compute service to run event-driven code without managing infrastructure.

Networking:

Azure Virtual Network: Isolated, private networks in Azure with fine-grained control over traffic between subnets. Azure Load Balancer: Layer-4 load balancer for distributing network traffic across service instances.

Storage:

Azure Blob Storage: Scalable, secure, and cost-effective object storage for unstructured binary and text data. Azure File Storage: Managed file shares for cloud or on-premises deployments.

Databases:

Azure SQL Database: Fully managed, intelligent, and scalable relational database service based on SQL Server. Cosmos DB: Globally distributed, multi-model NoSQL database service with low latency and tunable consistency.

Security and identity:

Azure Active Directory: Identity and access management service for centralized user management, authentication, and authorization. Azure Key Vault: Secure storage and management of cryptographic keys, secrets, and certificates.

Developer and management tools:

Azure DevOps: All-in-one solution for planning, collaboration, build, test, and deployment of applications. Azure Monitor: Comprehensive, full-stack monitoring service for application and infrastructure performance.

Analytics and IoT:

Azure Stream Analytics: Real-time, complex event-processing service for streaming data. Azure Data Factory: Data integration service for orchestrating and automating data movement and transformation.

AI and Machine Learning:

Azure Machine Learning: Scalable, managed platform for model development, deployment, and management. Azure Cognitive Services: Suite of AI services for vision, speech, text, and language understanding.

Iaas - Infrastructure as a Service, Paas - Platform as a Service, and Saas - Software as a Service are the three different categories of cloud computing services offered by Azure.

Here’s a quick look at IaaS vs PaaS vs SaaS:

Infrastructure as a Service (IaaS): With IaaS, Azure provides users with virtual machines, storage, and networking capabilities, allowing them to run their own operating systems and applications in the cloud.

Platform as a Service (PaaS): With PaaS, Azure provides users with a platform for building, deploying, and managing applications without the need for the underlying infrastructure. Azure manages the infrastructure, including servers, storage, and networking, and users can focus on developing their applications.

Software as a Service (SaaS): With SaaS, Azure provides users with ready-to-use software applications that are hosted in the cloud. Users can access the applications through a web browser or mobile app.

In Azure, a virtual machine (VM) is a computing environment that behaves like a physical computer but is implemented entirely in software. A VM runs on physical hardware, but its operating system, software applications, and data are all stored in a virtualized environment.

Azure provides a range of virtual machines that users can choose from with different sizes, configurations, and operating systems.

To create a virtual machine in Azure, follow these general steps:

• Sign in to the Azure portal at https://portal.azure.com.
• Click on the "Create a Resource" button on the left-hand menu and search for "Windows Server" or "Linux" depending on the operating system you want to use.
• Select the appropriate virtual machine image and size based on your requirements. You can choose from different sizes depending on CPU, memory, and storage capacity.
• Configure the VM settings, including the name, username, and password for the administrator account, network settings, and disk settings.
• Review the summary page and click on "Create" to create the virtual machine.
• Once the VM is created, you can connect to it using Remote Desktop for Windows virtual machines or SSH for Linux virtual machines.

The Azure Resource Manager (ARM) is a service offered by Microsoft Azure to help users with the management and organization of resources deployed on the cloud. It provides a way to group related resources like virtual machines, storage accounts, and network interfaces into logical units for easier management and deployment.

It also enables you to define dependencies between resources, so you can ensure that resources are deployed in the correct order and that any prerequisites are met. ARM provides a centralized and consistent way to manage Azure resources, making it easier to scale and manage complex applications and infrastructures in the cloud.

It provides a unified API for managing resources in a single, coordinated manner. It also enables you to tag and categorize resources and apply policies to enforce compliance across your organization.

A virtual network in Azure is a representation of the physical network in the cloud. It enables you to establish a secure connection and isolate Azure resources such as storage accounts, app services, and virtual machines from other networks within your Azure subscription. It enables the creation of a private network in the cloud. You can also define IP addresses, subnets, security policies, and routing tables.

To create a virtual network in Azure, you must follow these steps:

• Log in to the Azure Portal (https://portal.azure.com/).
• Click on the "Create a resource" button (+) in the top left corner.
• In the search bar, type "Virtual network" and select "Virtual network" from the dropdown list.
• Click on the "Create" button.
• In the "Basics" tab, enter a name for the virtual network and select the subscription, resource group, and region where you want to create the virtual network.
• In the "IP addresses" tab, specify the IP address range and subnet(s) for the virtual network.
• In the "Security" tab, configure network security groups (NSGs) to control inbound and outbound traffic to resources in the virtual network.
• Click on the "Review + Create" button to review your settings.
• Click on the "Create" button to create the virtual network.

Once the virtual network is created, you can add resources, such as virtual machines or storage accounts, to the virtual network and configure their network settings.

Cloud service roles comprise a collection of application and configuration files. Azure offers two types of roles: administrative and technical.

Web role: This role provides a dedicated IIS (Internet Information Services) web server for the automatic deployment and hosting of front-end websites.

Worker roles: The worker roles allow the programs hosted within them to run asynchronously for extended periods, are independent of user interactions and do not typically use IIS. They are also suitable for carrying out background tasks. The applications are run independently.

It is common to come across this Azure data engineer interview question.

IaaS: This stands for "Infrastructure as a Service." The infrastructure serves as a platform for hosting applications. It provides a collection of capabilities such as operating systems, network connectivity, and so on that are at the infrastructure level and are offered as a pay-per-use policy. Azure VM, VNET, and other examples are available.

PaaS: PaaS stands for "Platform as a Service," and it is primarily concerned with providing underlying infrastructure abstraction to developers to enable faster application development without dealing with hosting maintenance. Azure online apps, storage services, cloud services, and so on are some examples.

SaaS: SaaS stands for "Software as a Service." It refers to programs that are delivered via the service delivery paradigm and are simply consumed and used by an enterprise. These applications are typically activated by charging the organization for their use or through advertisements. Applications such as Office 365, Gmail, and SharePoint Online are examples.

With a virtual network, you can control network traffic flow between resources, implement security policies to restrict access to resources, and configure network address translation (NAT) to allow access to resources from the internet. You can also connect your virtual network to an on-premises network using a VPN gateway, an ExpressRoute circuit, or a point-to-site VPN connection.

Virtual networks provide scalability and flexibility by enabling you to create multiple virtual networks and creating a connection between various networks. Azure virtual networks provide a secure and flexible way to connect and isolate resources in the cloud, enabling you to build and deploy complex applications and services with ease.

It is a command-line utility for creating service package files. The tool also assists in preparing the application for deployment in Microsoft Azure or a computing emulator.

Every cloud service project contains a.cscfg file, which is essentially the cloud service configuration file generated by the cspack program and is mostly used to store:

• The number of role instances required for each role's deployment in the project
• The certificates' thumbprint
• Configuration and settings are user-defined

Azure Cognitive Services Containers are a way to deploy and run Azure Cognitive Services APIs within Docker containers. It is a suite of pre-built APIs and services that enable developers to add various features to improve the performance of their applications.

By deploying Azure Cognitive Services APIs within Docker containers, developers can take advantage of the benefits of containerization, such as improved scalability, easier management, and increased portability. It can be deployed and managed using container orchestration platforms like Kubernetes or Docker Swarm.

Azure app service is an HTTP-based service from Microsoft Azure for building, deploying, and hosting web and mobile applications as well as REST APIs. With it you can develop apps using different programming languages such as Java, PHP, Python, Ruby, Node.js, and .NET, among others.

The Azure app service provides a managed environment for hosting applications without the need to manage the underlying infrastructure. This allows developers to focus on application development and deployment.

You get a wide range of features, capabilities, and benefits with the Azure app service. Here are some of the prominent features:

• Multiple deployment options, including code deployment from GitHub, Bitbucket, or Azure DevOps, container-based deployment, and automated deployment from Visual Studio or Azure CLI.
• Scalability options include horizontal scaling with automatic load balancing, and vertical scaling with multiple instance sizes.
• Integration with other Azure services like Azure Active Directory, Azure SQL Database, and Azure Storage.
• Custom domain support, SSL certificates, and SSL binding.
• Built-in monitoring and logging capabilities via integration with Azure Monitor, Application Insights, and Log Analytics.

Azure Time Series Insights is a powerful data analytics tool from Microsoft that enables you to collect and store data at a high frequency, typically from sensors or other devices, and analyze that data in real-time to gain insights into performance, anomalies, and trends.

The data is stored in a time-series database that allows for fast and efficient querying of historical data as well as real-time data. Time Series Insights provides a user-friendly, web-based interface that allows you to visualize and analyze data using interactive dashboards and charts.

With Azure SQL Database, you can provision databases in minutes, scale up or down as needed, and benefit from automatic patching, backup, and high availability. It offers a range of features and capabilities, including:

• Compatibility with T-SQL which allows developers to use familiar tools and techniques to build and deploy applications.
• Built-in security features, including advanced threat protection, data encryption, and access control.
• Automatic backups, point-in-time restore, and geo-replication which provides data protection and disaster recovery capabilities.
• Support for multiple deployment options, including single databases, elastic pools, and managed instances.

The Azure Service Level Agreement (SLA) is a contract that ensures or guarantees that when two or more role instances of a role are deployed on Azure, access to that cloud service is assured at least 99.9 percent of the time.

It also indicates that if the role instance process is not in the operating state, such processes will be detected and corrective action will be taken 99.9 percent of the time.

Azure Storage is a modern cloud storage solution from Microsoft that offers storage solutions to contemporary requirements. It provides high availability, durability, scalability, and security for various data assets on the cloud.

Azure Storage gives access to data objects from anywhere in the world using HTTP or HTTPS through REST API. You also get client libraries with Azure Storage to build applications/services using various languages like Java, Python, C++, JavaScript, Go, and .NET.

Azure Storage service provides many benefits and features, including:

Scalability: It allows you to scale your storage capacity as needed without the need to provision or manage additional hardware.

High availability: It provides multiple layers of redundancy, ensuring that your data is available and accessible at all times.

Security: It provides built-in security features, including encryption at rest and in transit, access control, and firewalls, to help protect data from unauthorized access.

Durability: It provides high durability for your data with multiple copies stored across different storage nodes and data centers.

Cost-effectiveness: It offers flexible pricing options based on your storage needs, allowing you to pay only for the storage you use.

Accessibility: Azure Storage data is highly accessible from anywhere in the world through HTTP or HTTPS. The service also offers scripting in Azure CLI or Azure PowerShell and easy data visualization solutions via Azure Portal.

There are three cloud deployment models available in Azure:

Public Cloud: The cloud provider owns the cloud infrastructure publicly. Server resources may be shared among multiple applications.

Private Cloud: You own the cloud infrastructure entirely or the cloud provider provides you with a unique service. For this, you must host your apps either on on-premise servers or on a dedicated server offered by the cloud provider.

Hybrid Cloud: This model is a hybrid of the private cloud and the public cloud. It involves using dedicated servers to process confidential, sensitive data and public cloud capabilities to host public-facing applications.

Azure and AWS are two of the most popular cloud computing platforms available today. While they offer many similar services, there are some key differences between them:

Market presence: AWS has been around since 2006 and enjoys the largest market share, while Azure, launched in 2010, is the second largest. Azure is growing at a faster rate as it benefits from its integration with other Microsoft products.

Service offerings: While both offer a comprehensive set of services, AWS provides a slightly wider range of features and options. However, Azure is catching up quickly, constantly expanding its range of services.

Integration with other products: Azure has better integration with other Microsoft products like Office 365, Dynamics 365, and Power BI. This makes it appealing to organizations already invested in Microsoft technologies. AWS, on the other hand, is more focused on open source and has a broader ecosystem of third-party integrations.

Hybrid cloud support: Azure has a stronger emphasis on hybrid cloud solutions with services like Azure Stack that enable organizations to build and deploy hybrid applications consistently across on-premises and cloud environments. AWS also supports hybrid cloud but primarily focuses on cloud-native applications.

Pricing: Both providers offer a pay-as-you-go pricing model, but the specific costs can vary depending on the services and features required. AWS tends to have more granular pricing options with per-second billing and savings plans, while Azure offers discounts and extended contract periods for enterprises.

User interface and usability: Azure is known for a more user-friendly portal interface, while AWS Management Console is more advanced and offers more customization options. AWS users often utilize third-party tools or command-line interfaces for usability improvements.

Azure Resource Manager (ARM) templates are JSON files that specify the configuration and infrastructure of a project within the Azure deployment. They help with the creation, deployment, and management of Azure resources as a group.

ARM templates can be used for defining and deploying a range of Azure resources, such as virtual machines and networks, databases, web apps, and storage accounts. Templates can be written using JSON syntax and can be stored in source control which makes it easy to version, share, and update templates as needed.

Azure Pipelines is a cloud-based continuous integration and continuous delivery (CI/CD) service from Microsoft. It lets users build, test, and deploy their applications to a destination of their choice, and supports all major languages and projects.

With Azure Pipelines, developers can create automated workflows that run tests, build and package applications, and deploy them to multiple environments, including on-premises servers, virtual machines, and cloud services.

Azure DevTest Labs enables developers to quickly create Azure environments for the purpose of deploying and testing their applications. The service offers easy provisioning of Linux and Windows machines in Labs via reusable templates which minimizes the cost of the entire process.

It also provides integration with CI/CD pipelines, resource management, and self-service capabilities to developers and testers. It provides a reliable and efficient way to manage testing environments and implement robust testing.

The Azure Functions premium plan is a premium offering from Microsoft that provides all the features included in the standard consumption plan in addition to others like enhanced performance, VNet access, and no cold start, among others.

The premium plan also offers virtual network integration or connectivity by allowing applications to get secure access to resources on the virtual network. Besides this, you also get longer runtime durations, premium instance sizes, and higher-density app allocation.

Azure Monitor is a complete monitoring solution that is used for the collection, analysis, and telemetry response to applications from the cloud and on-premise platforms.

It helps collect and aggregate the data from different layers and components of the system to a common platform. Azure Monitor is generally used to optimize the performance and availability of applications and services on the cloud.

Azure Site Recovery is a disaster recovery solution that helps organizations protect and recover their workload in situations of outage or disruption. It provides a simple, automated, and scalable solution for replicating and recovering virtual machines, physical servers, and applications to Azure or other data centers.

Recovery processes, deploy replication, and failover are some of the methods used by Azure Site Recovery to keep applications running in case of planned and unplanned outages.

NSG is an abbreviation for Network Security Group, which contains a set of ACL (Access Control List) rules that allow or deny network traffic to subnets, NICs (Network Interface Cards) linked to a subnet, or both. When NSG is connected to a subnet, the ACL rules apply to all virtual machines in that subnet.

Azure ExpressRoute is a dedicated service from Azure that helps establish private and exclusive connections from an on-premises network to Azure data centers using a connectivity provider.

When using ExpressRoute, connections are not routed through public networks. Instead, a dedicated, private network is used to provide users with faster speeds, higher reliability, improved security, and consistent latency.

Azure CDN stands for content delivery network. It is a globally distributed server network for the efficient delivery of web content to end-users. It enables organizations to deliver high-bandwidth content to users around the world with low latency and high availability.

Azure CDN provides a globally distributed network of servers with content cached on edge servers, close to end-users, thereby reducing the distance and time required to deliver the content.

Azure Scheduler enables you to perform background trigger events or operations such as calling HTTP/S endpoints or presenting a message on the queue on any schedule.

With Azure Scheduler, tasks in the cloud call services within and outside of Azure to perform on-demand jobs that are frequently repeated on a regular schedule or to start those jobs at a future set date.

Azure AD Connect is a useful tool to connect on-premises applications for accomplishing and meeting hybrid identity goals. Azure AD Connect allows you to integrate on-premises identity infrastructure with Azure AD, which is the cloud-based identity and access management service provided by Microsoft. By doing so, you can use your existing Active Directory credentials to access cloud resources like Office 365, Azure, and other Microsoft online services.

Azure Traffic Manager is a traffic load balancer based on DNS. It is used to route incoming traffic to multiple end-points across various Azure regions globally. It offers increased responsiveness and high availability to your public endpoint through optimized scaling of traffic.

Traffic Manager uses DNS to direct client requests to the best endpoint based on the routing method you choose. You can also use it to route traffic to specific endpoints based on factors like performance, cost, or regulatory compliance requirements.

Azure Backup is a cloud-based backup and recovery solution that enables you to backup and restore data for applications, virtual machines, and files. You can automatically allocate and manage the backup storage. The pay-as-you-use model helps with cost efficiency.

Azure Backup can be used for various scenarios, including on-premise servers, Azure virtual machines, SQL databases, and Azure files.

Azure Kubernetes Service is a fully managed container orchestration service from Microsoft used for simplifying the deployment and management of containerized applications via Kubernetes. It offers an easy way for developers to develop and deploy cloud-native applications at the edge or Azure cloud platform through built-in-code-to-cloud pipelines as well as guardrails.

This service eliminates the complexity and overhead of managing Kubernetes infrastructure, allowing developers to focus on building and deploying applications.

Azure Service Health delivers real-time information by advising on the health of your cloud environment. It informs about the status of Azure services and alerts you to any service issues, planned maintenance, or outages that could drastically impact the performance of the Azure environment.

Azure Service Health helps minimize downtime, provides proactive monitoring, and ensures service compliance, all of which go a long way in optimizing the performance of your Azure environment.

The benefits of Azure Key Vault are as follows:

Secure storage and management of keys and secrets: Azure Key Vault provides a secure and scalable solution for storing and managing keys, secrets, and certificates used in cloud applications.

Centralized management: It allows you to centrally manage access to secrets and keys, simplifying the management and audit of sensitive data.

Integration with Azure services: It integrates with other Azure services such as Azure virtual machines, Azure web apps and functions, Azure data lake, and Azure kubernetes service among others.

Key management and rotation: Azure Key Vault provides built-in capabilities for key rotation and management, ensuring that your keys are always up-to-date and secure.

Azure Arc-enabled servers are used to extend Azure management capabilities and apply Azure services consistently across hybrid and multi-cloud environments. They help organizations manage their resources—running on-premises, in the cloud, or at the edge—regardless of the infrastructure provider.

Some of the key features of Azure Arc-enabled servers include:

Centralized Management: Azure Arc unifies the management of resources across on-premises, Azure, and other cloud environments using Azure Resource Manager. This makes it easier to monitor, manage, and maintain resources, as well as apply consistent governance across the entire infrastructure.

Policy Compliance and Security: With Azure Arc, organizations can apply uniform policies and security measures across their resources, ensuring consistent compliance and risk assessment. Azure Policy and Azure Security Center help users identify and respond to threats while complying with regulatory requirements.

Inventory and Resource Organization: Azure Arc provides a single pane of glass to maintain an inventory of servers, categorize them using tagging, and group and manage them as required. This allows for quicker discovery and management of resources.

Role-Based Access Control (RBAC): Azure Arc extends Azure's RBAC capabilities to manage access control permissions across different environments. It helps organizations fine-tune access control by assigning user roles, ensuring that only authorized users can take actions on the resources.

Extension Management: Azure Arc enables organizations to use Azure VM extensions for patch management, security, agent-based monitoring, and other operations on non-Azure machines.

The primary advantages of Azure Cosmos DB are as follows:

Global distribution: Cosmos DB enables you to distribute data across multiple regions which ensures low latency and high availability for your applications.

Multi-model: It provides support for multiple data models, including document, key-value, graph, and column family. This allows you to use the right data model for your application’s needs.

Scalability: Cosmos DB provides horizontal scaling for data which enables you to scale your databases to handle millions of requests per second.

Security: It provides built-in security features, including network isolation, encryption at rest, and role-based access control.

Azure Front Door is a contemporary content delivery network on the cloud that delivers high scalability, performance, and security to users when accessing content and applications. It works by using Microsoft's global network of edge nodes to route traffic to the nearest available backend service.

It provides a range of routing options, including URL-based routing, geo-routing, and weighted traffic distribution. This allows businesses to optimize their application delivery by sending traffic to the closest available backend service or the service with the lowest latency.

Azure Event Hubs offers several benefits as mentioned below:

High throughput and low latency: Azure Event Hubs provides high throughput and low latency for collecting and processing real-time data which allows you to handle millions of events per second.

Scalability: It provides horizontal scaling which enables you to scale your event hub to handle increasing volumes of data.

Integration with other Azure services: It integrates with other Azure services, such as Azure Stream Analytics, Azure Functions, and Azure Logic Apps. This lets you easily process and analyze streaming data in real-time.

Built-in security: Azure Event Hubs provides built-in security features, including network isolation, encryption at rest, and role-based access control which ensures that your data is secure.

Azure Stream Analytics is a real-time data processing and fully managed stream processing service. The stream analytics engine enables you to analyze and process high volumes of streaming data from various sources like devices, sensors, social media feeds, and other data streams in real-time.

With Azure Stream Analytics, you can easily create and run complex queries over streaming data using SQL-like language. This can include real-time aggregations, filtering, transformations, and more. You can also easily integrate the results of your stream analytics with other Azure services like Azure Machine Learning, Azure Data Lake, Azure Event Hubs, and Azure Functions.

Here are some of the benefits of Azure Cognitive Services Containers:

Consistent infrastructure: You get an immutable and consistent infrastructure that keeps the system parameters predictable, thus making it easier to manage and troubleshoot issues.

Control on data: You have complete control over your data and can choose how and where the data gets processed by the Cognitive Services. This also means you can ensure that the data complies with your organization's policies and regulations.

Model updates: You can choose when to update your models and ensure that they are tested and validated before they are deployed.

Portable mechanism: Azure Cognitive Services Containers are based on Docker containers, which means that they can be deployed on any platform that supports Docker. This makes it easier to deploy cognitive services across multiple environments, including on-premises and cloud-based environments.

Scalability: Azure Cognitive Services Containers are highly scalable. You can deploy multiple containers to handle increased load and ensure that your cognitive services can handle large amounts of data.

Azure Cognitive Search is a cloud-based search service that offers developers tools and APIs for a comprehensive search experience across private and diverse content on mobile, enterprise, and web applications. It helps developers add sophisticated search capabilities to the applications.

The service uses AI technologies, such as natural language processing (NLP) and machine learning (ML), to provide high-quality search results. Azure Cognitive Search can be integrated with various data sources like Azure SQL Database, Azure Blob Storage, and Azure Table Storage.

Azure Batch is a cloud-based service provided by Microsoft Azure that enables developers to run large-scale parallel and high-performance computing (HPC) workloads. It allows you to execute batch jobs in parallel, distribute them across multiple virtual machines (VMs), and automatically scale up or down the number of VMs based on the demand for computing resources.

Azure DevOps provides a set of tools and processes for development collaboration among cross-functional teams and optimizes the delivery process. The platform includes a range of services, including version control, continuous integration and delivery (CI/CD), project management, and testing.

Azure DevOps is highly customizable and integrates with many development tools and platforms, including Visual Studio, Eclipse, Jenkins, and GitHub. It provides a unified platform for developers, testers, and project managers to collaborate and deliver high-quality software products faster and more efficiently.

Azure Event Grid is a robust service that allows developers to build event-based architectures and offers reliable delivery of messages on a large scale. You can build event-driven apps on a serverless architecture, thereby eliminating the need for polling and related latency as well as cost.

Azure Event Grid supports events from various Azure services, such as Azure Blob Storage, Azure Event Hubs, and Azure Functions, as well as custom events.

Azure Logic Apps is a prominent PaaS - platform as a service - for integration based on containerized runtime. It enables the creation of automated workflows for the integration of different systems and services.

With Logic Apps, you can deploy and run apps across multiple platforms, thereby increasing scalability and portability while automating the functioning of workflows.

The purpose of Azure Migrate is to provide a simplified, automated, and unified way to assess and migrate on-premises workloads to Azure. It provides a central hub to discover, assess, and migrate resources, helping organizations to migrate their applications, databases, virtual machines, and servers to Azure in a seamless and efficient manner.

Azure Migrate also offers a range of features to simplify the migration process, including the ability to group and sequence migrations, track progress, and validate migrated resources. It also provides integration with other Azure services, such as Azure Site Recovery, to help organizations ensure business continuity during the migration process.

Azure provides the IoT Hub service to optimize and manage IoT development. It is a cloud-based service for securely managing IoT devices and associated data. Using Azure IoT Hub, you can connect, monitor, and manage various IoT devices at scale. You can also execute secure transmission of data from these devices across Azure services for the purpose of processing and analysis.

Azure IoT Central is a custom-made UX and API platform to connect and manage IoT devices at scale, thereby offering consistent data that can be used in business analytics to generate highly valuable insights. This service provides a pre-assembled platform by bringing together different functionalities under one umbrella, making it easy to configure, connect, and manage IoT devices and services.

With centralized management and reliable visual data analysis, developers can use this service to bridge the gap between IoT data and business applications to drive insights into meaningful action.

Azure Data Share is a high-utility service that allows organizations to share data securely with various partners and customers. This, combined with the management of data-sharing options and monitoring capabilities, enables collaboration and data-driven decision-making among different parties.

Azure Digital Twins is an IoT platform for the digital creation of real-world entities through accurate and highly reliable representation. With Azure Digital Twins, you can create a virtual representation of your physical environment and use it to model, simulate, and analyze different scenarios to optimize performance, efficiency, and sustainability.

Azure Digital Twins offers a range of features:

Creating a digital twin: It provides a modeling language and tools to create a digital twin of your physical environment.

Connecting sensors and devices: It enables you to connect sensors and devices in your physical environment to your digital twin.

Simulate and analyze: It provides simulation and analysis tools that let you simulate different scenarios and analyze the performance of your environment.

Integrate with other Azure services: It integrates with other Azure services, such as Azure IoT Hub, Azure Stream Analytics, and Azure Machine Learning, enabling you to build powerful end-to-end solutions.

Azure Spring Cloud is a fully managed service from Azure that is used to deploy and run spring boot applications at scale. Spring Boot is a popular framework for building microservices-based applications in Java. Meanwhile, Azure Spring Cloud provides a platform for hosting, deploying, and managing these applications in a cloud environment

The Azure Container Registry is a cloud-based registry service that enables the creation, storage, and management of container images within a private registry for various types of container deployments. Container images are a lightweight, portable package that have everything needed to run an application, including the code, dependencies, and configuration.

There are several benefits of Azure Container Registry, such as:

Secure storage: It provides secure storage for container images, including built-in support for role-based access control, encryption, and vulnerability scanning.

Integration with Azure services: It integrates with other Azure services like Azure Kubernetes Service, Azure Web Apps, and Azure Functions.

High availability: It provides high availability and scalability which ensures that container images are always available to deploy.

Built-in automation: It provides automation capabilities, including image lifecycle management, webhooks, and registry-level tasks.

Multiple registry types: It supports multiple registry types, including Docker Hub-compatible registries, Helm Chart repositories, and Open Container Initiative (OCI) registries.

Azure Databricks is a big data processing platform used to simplify the process of building and deploying large-scale data analytics and machine learning solutions. It is a fully-managed service that enables you to create various analytical workloads using data lakehouses while allowing governance across the complete data estate.

You can use Azure Databricks for data engineering, data science, AI, machine learning, and data querying analytics, among others.

Azure Stack Hub is an extension of the Azure public cloud. It provides a hybrid platform for delivering services from the datacenter. It offers a consistent cloud infrastructure, application platform, and developer experience across both Azure public cloud and on-premises environments. Services provided include SQL server databases, virtual machines, and marketplace items.

Azure HDInsight is a fully managed, open-source, cloud-based analytics service for enterprises. It enables organizations to process and analyze large amounts of data using a scalable, secure, and cost-effective cloud platform. It provides a big data and advanced analytics solution based on popular open-source technologies like Apache Hadoop, Spark, Hive, HBase, and Storm.

With HDInsight, organizations can run Hadoop workloads without worrying about the underlying infrastructure and maintenance tasks, such as patching, upgrading, and scaling.

Azure Automation provides a wide range of features to automate several tasks, thus reducing the time and resources required for managing cloud infrastructure. Some of the prominent benefits of Azure Automation include reduced manual effort, improved consistency and scalability, better compliance and security, and significant cost savings.

Azure Notification Hubs is a cross-platform notification service that allows you to send push notifications to millions of devices across several platforms such as Android, iOS, Windows, and Amazon Kindle.

The main function of Azure Notification Hubs is to simplify the process of sending push notifications to a large number of devices by providing a scalable, reliable, and efficient platform. It allows you to send personalized and targeted notifications to specific users or groups of users based on their preferences, behaviors, or locations.

Azure Traffic Manager is a DNS-based, global traffic management service that allows you to distribute traffic over multiple endpoints. Some of its main benefits are:

High availability: It monitors the health of your endpoints and automatically reroutes traffic to healthy endpoints in case of failures or downtime to ensure high availability for applications and services.

Performance optimization: It uses a variety of routing methods, such as performance-based routing, geo-based routing, and weighted round-robin, to optimize the performance and latency of applications and services for end-users.

Scalability: It can handle high volumes of traffic and can scale up or down based on demand, ensuring that your applications and services can handle spikes in traffic and maintain performance.

Cost-effectiveness: Azure Traffic Manager can help reduce the cost of delivering applications and services globally by optimizing the use of resources and reducing the need for dedicated infrastructure.

Azure Stack Edge is a hybrid cloud computing device that brings Azure services to the edge, allowing customers to process and analyze data closer to where it is generated. The primary components of Azure Stack Edge are as follows:

Azure Stack Edge resource: This is the Azure resource that you deploy in your Azure subscription to manage the Azure Stack Edge Pro GPU physical device. It includes the Azure Stack Edge Management Portal, which provides a web-based interface for managing and monitoring the device, deploying workloads, and configuring networking and security settings.

Azure Stack Edge Pro GPU physical device: This is a physical appliance that provides high-performance GPU computing capabilities for edge workloads. It includes a powerful processor, solid-state storage, network connectivity, and one or more NVIDIA GPUs.

Local web UI: The Azure Stack Edge Pro GPU device includes a local web-based user interface that can be used to monitor and manage the device directly from the device itself.

Azure Backups provide three replication types for high availability of data: locally redundant storage (LRS), geo-redundant storage (GRS), and zone-redundant storage (ZRD).

Locally redundant storage (LRS): This option provides redundancy within a single data center, which means data is replicated three times within that data center.

Geo-redundant storage (GRS): This storage option replicates data to a secondary data center in a different geographic location from the primary data center. Data is stored in two separate physical locations, providing better protection against data loss due to disasters.

Zone-redundant storage (ZRD): This option provides replication of data across multiple zones within a region, providing higher availability and resiliency for applications and data.

Azure Sphere is a high-level, secure application platform comprising in-built security and communication features for devices connected via the internet. It is specifically designed with security as one of its core features.

With Azure sphere, you get multiple layers of security including hardware security, secure development tools, operating systems, and cloud-based monitoring. Users can leverage these security features to build powerful and highly secure IoT applications.

Azure Data Factory is a cloud-based data integration service that enables developers to create and manage workflows that are responsible for moving and transforming data across various platforms. Here are some ways in which it helps in data integration and transformation:

Data integration: It allows developers to connect to data sources such as SQL Server, Azure Blob Storage, Azure Data Lake Storage, and more.

Data transformation: It provides a drag-and-drop visual interface that allows developers to build complex data transformation logic using a variety of built-in activities.

Data orchestration: It provides a rich set of scheduling and monitoring features that allow developers to orchestrate data workflows at scale.

Integration with other Azure services: It integrates with other Azure services like Azure Data Lake Analytics, Azure HDInsight, Azure SQL Database, etc.

Data governance: It provides a number of features for managing data governance and compliance, including data lineage, data encryption, and access controls.

Azure Firewall is a managed, cloud-native network security service that helps protect virtual network resources. It offers stateful and stateless packet filtering for inbound and outbound traffic.

It provides protection at different levels including network traffic control, application level protection, threat intelligence support, and a centralized management system to deal with various attacks.

Azure Firewall plays a critical role in network security in the cloud by providing organizations with the ability to control network traffic, block malicious traffic, and protect their resources from security threats.

Azure Data Explorer, also known as Kusto, is a fast, highly scalable, and fully managed data analytics service designed for real-time analysis of large volumes of streaming data. It is primarily used for ingesting, storing, analyzing, and visualizing diverse data types in near real-time. Some common use cases of Azure Data Explorer include:

Log and Telemetry Data Analysis: Azure Data Explorer allows organizations to ingest and analyze log and telemetry data generated by applications, services, and infrastructure components. It helps in diagnostics, troubleshooting, monitoring performance, and identifying usage patterns.

Time Series Data Analysis: Azure Data Explorer is highly optimized for time series data, making it suitable for analyzing time-stamped events, trends, and patterns. This can help organizations detect anomalies, forecast, and make data-driven decisions.

Real-Time Data Streaming: Azure Data Explorer supports real-time data ingestion from various sources, such as IoT devices, web applications, or social media feeds. By ingesting, processing, and analyzing data in real-time, it enables organizations to gain insights and respond quickly to emerging trends or issues.

Customer Analytics and Personalization: Azure Data Explorer enables analysis of customer behavior and usage patterns, which can help businesses tailor their products, services, and marketing strategies to better suit their customers' needs.

Security Analysis and Threat Monitoring: Azure Data Explorer can process and analyze security logs and events, helping organizations identify security vulnerabilities, track malicious activity, and detect threats. This helps enhance the overall security posture and enables incident responders to handle threats effectively.

Integration with Azure Services: Azure Data Explorer smoothly integrates with other Azure services, such as Azure Monitor, Azure Data Factory, Azure Stream Analytics, and Power BI, allowing for streamlined ingestion, processing, analytics, and data visualization.

Azure Blueprints is a service from Microsoft Azure that offers a set of built-in components that enable developers to quickly build and scale up new environments with the assurance of complying with organizational policies.

With Azure Blueprints, organizations can define a set of standard configurations, policies, and best practices that can be easily and consistently deployed across multiple subscriptions and environments. It provides a set of pre-built templates that can be used as a starting point or be customized to meet specific requirements.

Azure Blueprint and Azure Resource Manager (ARM) Template are both used to define infrastructure configurations and automate resource deployment in the Azure cloud, but they serve different purposes and have distinct features.

Azure Dedicated Host is a Microsoft Azure service that offers physical servers exclusive and dedicated to your specific organization and workloads. Customers can run their workloads on dedicated hardware that is not shared with other tenants, thus leading to enhanced security, optimized performance, and full compliance.

Azure Dedicated Host differs from other Azure deployment options in that the dedicated host provides clients with more control over the underlying infrastructure. In contrast, other Azure deployment options, such as virtual machines or container instances, run on a shared infrastructure that is managed by Azure.

Azure Files can be used to enable file sharing across multiple VMs or cloud services in several ways:

By using the SMB protocol: It supports the Server Message Block (SMB) protocol which allows Windows-based virtual machines to mount Azure Files shares as network drives. This enables multiple virtual machines to access the same set of files.

By using the NFS protocol: It also supports the Network File System (NFS) protocol which allows Linux-based virtual machines to mount Azure Files shares as file systems. This enables Linux-based applications to access the shared files.

By using REST APIs: Azure Files also provides REST APIs that allow applications running on any platform to access files stored in Azure Files shares.

Azure Information Protection (AIP) is a cloud-based service that provides data protection and classification capabilities for organizations. It enables users to label and classify data based on its sensitivity, and then apply policies to control access and usage of that data. AIP also provides the ability to track and revoke access to data, even after it has been shared outside the organization's network.

The prominent features of AIP are as follows:

Classify data based on sensitivity: It offers the ability to classify data based on its sensitivity by applying labels or tags to documents and emails.

Data protection: It helps protect data at rest and in transit by applying encryption and access policies. It provides control over who can access the data, where it can be accessed from, and what users can do with the data.

Add visibility and control: It provides visibility and control over data usage by enabling organizations to track and monitor how data is accessed and shared.

Secure collaboration: It offers secure collaboration features that let users share sensitive data with others inside and outside the organization. It provides controls over how data is shared, who can access it, and what they can do with it.

Ease of use: It is designed to be easy to use and integrate with existing workflows.

Azure NetApp Files is a fully-managed cloud file storage service designed to provide high-performance file shares for enterprise workloads in the cloud. It is a first-party service powered by NetApp technology and offers enterprise-grade features such as data tiering, backup and restore, and disaster recovery.

The service provides high-performance, enterprise-grade file storage for mission-critical workloads in the cloud.

Microsoft Azure offers the Azure Machine Learning service that allows developers and data scientists to build, train, deploy, and handle machine learning models at scale. It provides tools and services for every stage of the machine learning lifecycle, from data preparation and model development to deployment and monitoring.

With Azure Machine Learning, users can choose from a range of algorithms and frameworks, including deep learning and neural networks. They can also access pre-built APIs for common machine learning tasks, such as text analytics and image recognition. In addition, the platform provides collaboration features for teams to work together on machine learning projects and automate workflows using Azure Pipelines.

Here are some of the prominent use cases for Azure Machine Learning:

Customer segmentation: It can be used to segment customers based on their behavior and preferences, allowing companies to personalize their marketing and improve customer retention.

Image recognition: It can be used to train models to recognize objects in images. This, in turn, can be applied to many use cases such as medical imaging, autonomous vehicles, and surveillance.

Natural language processing: It can be used to analyze and understand unstructured data like text and speech which can be applied to tasks such as chatbots and language translation.

Recommendation systems: It can be used to build recommendation systems that suggest products or content to users based on their past behavior and preferences in order to increase engagement and sales.

Azure Service Fabric is a distributed systems platform used to build and manage scalable and dependable microservices-based applications. It also offers effective solutions to prominent challenges involved in the development and management of cloud-native applications.

Service Fabric has a range of features, such as automatic scaling, rolling upgrades, failure recovery, and load balancing, to ensure that your application is always available and responsive.

Example of creating a Service Fabric Cluster using Azure CLI:

Here are some of the best practices to optimize the performance of Apache Spark jobs in Azure Databricks:

Choose the appropriate data abstraction: Depending on the use case, choosing the right data abstraction can significantly impact performance. For example, using DataFrames can be faster than RDDs for complex queries and transformations.

Use an optimal data format: The data format used can impact performance, especially when dealing with large datasets.

Use caching: Caching frequently used data in memory can improve query performance by reducing the time spent reading data from the disk.

Use memory efficiently: Consider using broadcast variables which allow variables to be cached on each node. This reduces the amount of data that needs to be transferred over the network.

Use bucketing: Bucketing data on a specific column can help speed up queries that require data to be sorted or grouped by that column. It does this by reducing the number of files that need to be read.

Azure Synapse Analytics is a cloud-based enterprise analytics service designed to provide quicker insights across big data systems and data warehouses. The service combines the best SQL technologies used across various frameworks such as Azure Data Explorer, Apache Spark technologies, and enterprise data warehousing.

This service helps businesses with complex data analytic requirements by providing a powerful, integrated platform for data warehousing, big data processing, and machine learning. Some of its features include unified analytics, integration with Azure services, security and compliance, high performance, and scalability.

Azure Event Hubs is a cloud-based event ingestion service that enables you to collect, process, and store large volumes of streaming data from various sources.

It is designed to handle millions of events per second, making it ideal for collecting and processing real-time data from a variety of sources like IoT devices, social media feeds, and application logs. The service helps in immediately responding to business challenges and building dynamic data pipelines.

Example of sending an event to an Event Hub using Azure SDK for JavaScript:

Azure Key Vault is a cloud-based service that provides secure storage and management of secrets, keys, and certificates. It enables developers to manage cryptographic keys used for encrypting application data and securely store passwords, connection strings, and other sensitive information.

Example of storing and retrieving a secret using Azure SDK for .NET:

Azure Advisor is a cloud service from Microsoft that offers valuable recommendations for optimizing Azure resources as well as improving overall performance. These recommendations help to increase reliability, operational efficiency, and security, and reduce costs.

Azure Advisor provides a dashboard that displays all of the recommendations, along with details about the impact, cost savings, and implementation steps.

Azure Private Link offers private connectivity from the virtual network to different services like Azure PaaS, Microsoft partner services, and customer-owned services.

By using Azure Private Link, you can access Azure services like Storage, Azure SQL Database, and Azure Data Factory over a private endpoint rather than over the public internet. Azure Private Link creates isolation, gives more control over the traffic, and provides the foundation for high-level monitoring, thus improving security.

Example of creating a private endpoint for an Azure Storage account using Azure CLI:

Once the private endpoint is created, you can configure your application to use the private IP address for accessing the Azure Storage account securely via your virtual network.

Azure Cosmos DB is a fully managed, globally distributed, NoSQL - relational database used in modern app development. It is a multi-model database service designed to offer high scalability, availability, and low latency access to cloud data for critical applications.

Example of querying data using the SQL API in JavaScript:

Azure Durable Functions is an extension of Azure Functions, enabling developers to implement stateful, long-running workflows using serverless architecture. It offers features like automatic state management, checkpointing, and support for long-running processes.

Example of a simple orchestrator function in Python:

Azure Data Share can be used to share data between multiple organizations. Here are the steps:

Create an Azure Data Share account: First, you need to create an Azure Data Share account in the Azure portal.

Define a data share: Next, you need to define a data share, which is a logical container for sharing a set of data with a specific set of recipients.

Invite recipients: Once you have defined a data share, you can invite recipients to access the shared data.

Configure data sharing: Once the recipients accept the invitation, you can configure the data sharing settings, such as the data source, the data schema, and the frequency of data sharing.

Monitor and manage data sharing: Azure Data Share provides a set of monitoring and management tools to help you track the usage and performance of your data shares.

Azure Application Insights is an Application Performance Management (APM) service that enables developers to monitor the performance, availability, and usage of their applications in real-time. It offers tools to diagnose performance issues, exceptions, and user behavior, providing comprehensive application monitoring.

Example of tracking a custom event in an ASP.NET Core application:

• Add the NuGet package: Microsoft.ApplicationInsights.AspNetCore.
• In Startup.cs, add the following code

• In your Controller:

Here are the steps to follow to create Azure automation:

Sign in to the Azure portal: Go to the Azure portal at https://portal.azure.com/ and sign in with your Azure account credentials.

Create a new Automation account: Click on the Create a resource button (+) in the top left corner of the screen. In the search box, type "Automation," and select Azure Automation from the results.

Configure the Automation account: In the Create Automation Account blade, enter the required information, such as the Subscription, Resource Group, and Automation account name. Choose a location for your Automation account and select the desired pricing tier.

Configure advanced settings: Under the Advanced tab, you can configure additional settings such as the type of account, the update frequency, and the hybrid worker group.

Review and create: After configuring the Automation account, review the settings and click on the Create button to create the new account.

Azure Cache for Redis is a fully-managed, in-memory cache service that facilitates high-performance and scalable architectures. The service is based on the popular open-source Redis cache technology and is optimized for low-latency and high-throughput data access.

Developers can store and retrieve data in memory, reducing the need to access disk-based storage and improving application performance.

Example of using Azure Redis Cache as a distributed cache in Python:

• Install the redis library: pip install redis
• Sample Python code:

Azure Service Bus is a fully managed integration message broker service that offers reliable and secure messaging and decoupling capabilities for cloud, hybrid, and on-premises solutions. It supports scenarios like order processing, asynchronous communication, and publish-subscribe patterns.

Example of sending a message to a Service Bus queue using Azure SDK for Java:

Example of creating a simple pipeline using the Copy Data wizard in the Azure Portal:

• Go to the Azure Portal and navigate to your Data Factory resource.
• Click on "Author & Monitor," then click on "Copy Data" to launch the Copy Data wizard.
• Set the properties for your pipeline, such as Task name, Task description, and Task schedule.
• Add a source data store to the pipeline, configure the connection information, and choose the dataset to copy from.
• Add a destination data store, configure the connection information, and choose the target dataset's mapping and schema settings.
• Review the mappings and settings, and select "Next" to configure settings like fault tolerance and performance settings.
• Review the summary and click "Next" to deploy and start the pipeline execution.

The Copy Data wizard will create and execute the pipeline according to the defined schedule after the deployment is complete

Azure Functions is a serverless computing solution from Microsoft that enables users to run event-driven code without worrying about the infrastructure. Users don’t need to worry about the deployment and maintenance of the servers as the cloud infrastructure offers all the updated resources essential for running the applications.

With Azure Functions, developers can write code that responds to various triggers, such as HTTP requests, messages in queues, and changes in databases or storage accounts. Azure Functions is compatible with all major languages including Python, Java, JavaScript, and C#, among others.

Example of a durable timer using Durable Functions in JavaScript:

• Install the durable-functions package: npm install durable-functions
• In the functions.json for the orchestrator function, add:

• In the orchestrator function (init.js):

Example of creating a build pipeline using a YAML definition file:

• In your project's code repository, create a file called azure-pipelines.yml at the root level.
• In the azure-pipelines.yml file, define the build pipeline, such as:

This example specifies that this pipeline will trigger on the main branch and install Node.js 14.x, install the npm dependencies, and run the build.

• Commit and push the azure-pipelines.yml file to your repository.
• In Azure DevOps, go to Pipelines > Create Pipeline, and follow the steps to create a pipeline using your YAML file in the source repository.

After creating the pipeline, Azure DevOps will automatically detect the azure-pipelines.yml file in the repository and use it as the build definition.

Azure Active Directory (Azure AD) is a cloud-based identity and access management service that enables organizations to manage users, groups, and devices securely. It provides features such as single sign-on (SSO), multi-factor authentication (MFA), role-based access control, and integration with various third-party applications. Azure AD ensures that only authorized users have access to your resources and applications while maintaining security and privacy.

Example of configuring authentication for an ASP.NET Core application:

• In the Azure Portal, create a new App registration in Azure Active Directory.
• Note the Application (client) ID and Directory (tenant) ID.
• In the Application Registration's Authentication tab, add a redirect URI and configure platform settings as needed.
• If the application requires access to Azure AD APIs, configure the API permissions in the API permissions tab.

Next, make the following changes in your ASP.NET Core application:

• Install the Microsoft.Identity.Web NuGet package.
• Edit appsettings.json and add the following configuration:

• In Startup.cs, modify the ConfigureServices method:

• In the Configure method:

• In the application's controller or middleware where you want to enforce authentication, add the [Authorize] attribute to the desired actions or controllers.

After these changes, your ASP.NET Core application will use Azure Active Directory for authentication and authorization. Users will be redirected to the Azure AD login page for authentication before accessing protected resources.

Azure Blob Storage is a cloud storage solution that allows you to store unstructured data like text and binary data on the cloud. It provides a scalable, secure, and cost-effective solution for the storage and management of large volumes of data.

With Blob Storage, you can serve documents or images on the browser, stream audio and video, store files for cloud access, backup data for disaster recovery, and store data for advanced analysis.

Azure Blob Storage lifecycle management provides automated and rule-based actions to transition blobs to different performance tiers (hot, cool, archive) or delete them when they are no longer needed. This helps optimize storage costs and data usage over time.

Example of a lifecycle management policy:

This policy will:

• Transition blobs to the cool tier after 30 days of modification.
• Transition blobs to the archive tier after 180 days of modification.
• Delete blobs after 365 days of modification.

• Azure API Management is a fully managed service that enables organizations to publish, secure, monitor, and analyze their APIs. It offers functionalities like API gateway, developer portal, API versioning, caching, and documentation support. Developers can create, import, and manage APIs with ease, while also configuring policies for advanced access control, request/response manipulation, and rate limiting.

Example of creating an instance of Azure API Management using the Azure Portal:

1. Visit the Azure Portal and sign in to your account.
2. Click on "Create a resource" in the left-hand menu.
3. Search for "API Management" and click on the result.
4. Click on the "Create" button.
5. Fill in the required information: Subscription; Resource group; Name; Location; Organization name; Administrator email
6. Click "Review + create" to review your settings and click "Create" to start the deployment process.
7. Wait for the deployment to complete, and your new API Management instance will appear in the list of resources in the chosen resource group.

The primary difference between Azure Functions and Logic Apps is that Logic Apps offers a visual designer and a wide array of connectors for integrating various services without code, while Azure Functions is a serverless compute service where you write custom code to handle events or respond to triggers.

Example of a simple Logic App workflow triggered by an HTTP request:

• In the Azure portal, create a Logic App.
• Open the Logic App's Designer and search for the "HTTP" connector in the search box.
• Choose the "When a HTTP request is received" trigger and add it to your workflow.
• Configure the trigger as needed (e.g., add JSON schema for expected input).
• Add an action to the workflow, such as "Send an email" using the Office 365 Outlook connector or any other action you prefer.
• Configure the action with the necessary inputs, for example, set the "To" and "Subject" properties of the email.
• Save the Logic App and grab the HTTP POST URL from the "When a HTTP request is received" trigger.
• Send an HTTP POST request to that URL with the appropriate data, and the Logic App will execute the workflow.

As a result, when an HTTP request is received, the email action will be executed based on the provided parameters.

Azure VPN Gateway and Azure Virtual WAN are networking solutions provided by Microsoft Azure, but they differ in terms of their capabilities and limitations.

Azure VPN Gateway is a service that offers secure connectivity between on-premises networks and Azure resources, using various types of VPN connections. However, it is limited to a maximum of 30 tunnels which makes it suitable for small-scale VPN connections.

In contrast, Azure Virtual WAN is a more comprehensive networking solution to connect multiple on-premises networks, Azure VNets, and remote sites. It can connect up to 1,000 branch connections per virtual hub with an aggregate of 20 Gbps per hub. This makes it suitable for large-scale VPN connections as it provides a more scalable and flexible networking solution.

Azure SQL Database is a fully managed, intelligent, and scalable cloud relational database service provided by Microsoft Azure that uses the Microsoft SQL Server engine. It offers built-in intelligence, high availability, dynamic scaling, and robust security features for data protection.

Example of performing CRUD operations using Entity Framework Core in a .NET Core application:

• Create a new .NET Core application and install the NuGet packages:

• Add a new connection string in appsettings.json:

• Create your model class (e.g., Customer.cs):

• Create a new ApplicationDbContext class that inherits from DbContext:

• In Startup.cs, add the following code to the ConfigureServices method:

• Run dotnet ef migrations add InitialCreate and dotnet ef database update to create the initial table schema in your Azure SQL Database.
• Now, you can perform CRUD operations, such as:

With this setup, you can perform CRUD operations on the Customer table in the Azure SQL Database using Entity Framework Core in your .NET Core application.

Azure Resource Graph service is designed to extend Azure resource management through highly performant and efficient resource exploration. It provides the capability of querying at scale over a specified subscription set to allow you to govern your environment effectively. It offers a unified API for querying Azure resources, regardless of the service or resource type.

Azure Logic Apps has two pricing plans: Consumption and Fixed. In the Consumption plan, you only pay for the executed action runs and trigger executions. It automatically scales with the number of executions and provides a shared environment. The Fixed plan offers dedicated throughput for your Logic Apps with predictable costs by using Integration Service Environment (ISE), which is ideal for scenarios with higher scale, isolation, and security requirements.

Example of creating a simple Logic App using Fixed plan:

• Create an Integration Service Environment (ISE) in the Azure portal.
• Create an empty Logic App within the ISE.
• Design your Logic App workflow as needed by adding triggers, actions, and connectors.
• Save and deploy the Logic App.

Once deployed, the Logic App will run using a dedicated environment with the specified fixed throughput.

Azure Data Lake Storage Gen2 is a scalable and cost-effective data lake storage solution built upon Azure Blob Storage. It provides advanced features such as hierarchical namespace support, high-performance, and fine-grained security to optimize the processing of big data and analytics workloads.

Example of creating a Data Lake Storage Gen2 account using Azure CLI:

Azure Virtual Machines Scale Sets makes it possible to build and manage a group of identical, load-balanced VMs in an elastic and automatically-scalable fashion based on defined rules and thresholds. It simplifies the deployment, management, and scaling of applications, ensuring high availability and performance.

Example of creating a Virtual Machine Scale Set using Azure CLI:

Example of training a simple model using the Azure Machine Learning Python SDK:

• Install the azureml-sdk Python package: pip install azureml-sdk
• Configure the training environment in your Python script:

• Prepare your training script (e.g., train.py). For example:

• Configure and submit the training run:

This example trains a simple logistic regression model on the Iris dataset using the Azure Machine Learning Python SDK and stores the trained model in the outputs folder.