Cloud & Infrastructure Topics
Cloud platform services, infrastructure architecture, Infrastructure as Code, environment provisioning, and infrastructure operations. Covers cloud service selection, infrastructure provisioning patterns, container orchestration (Kubernetes), multi-cloud and hybrid architectures, infrastructure cost optimization, and cloud platform operations. For CI/CD pipeline and deployment automation, see DevOps & Release Engineering. For cloud security implementation, see Security Engineering & Operations. For data infrastructure design, see Data Engineering & Analytics Infrastructure.
Microsoft Technology and Azure Ecosystem
Knowledge of Microsoft technology platforms and the Azure cloud ecosystem and how those platforms shape technical role requirements and candidate signals. This includes familiarity with core Azure services such as virtual machines and compute services, containerization and orchestration including Kubernetes based services, serverless compute and functions, managed databases and storage services, identity and access management, networking and security services, and platform services for artificial intelligence and machine learning. Recruiters should also understand common frameworks and languages used by teams building on Microsoft platforms such as dot net and C sharp, TypeScript and Python, and tooling such as Azure DevOps, infrastructure as code, and monitoring and observability solutions. For recruiting this includes knowing what resume indicators and project examples demonstrate hands on Azure experience, relevant certifications and sourcing keywords, and how to translate technical requirements into assessment criteria for cloud, platform, and site reliability engineering roles.
Microsoft Azure Fundamentals
Tests basic knowledge of cloud computing concepts and the Microsoft Azure platform. Candidates should understand why organizations adopt cloud infrastructure, the differences between infrastructure as a service, platform as a service, and software as a service, and common Azure service categories such as compute, storage, databases, networking, identity and access management, container orchestration, and serverless options. Candidates should also be prepared to discuss high level trade offs such as cost versus operational overhead, region and availability considerations, and typical application patterns that run on Azure.
Azure and Google Cloud Platforms
Knowledge of core cloud platform concepts and the managed services offered by Microsoft Azure and Google Cloud Platform. Candidates should understand compute options including virtual machines, managed container services, and serverless functions; platform as a service offerings; managed relational and non relational databases; object, block, and file storage; and messaging and eventing services. They should know networking fundamentals such as virtual private cloud or virtual networks, subnets, load balancing, peering, routing, and firewall rules, as well as identity and security topics including identity and access management, role based access control, key management, and encryption. Candidates must be able to map and compare common service equivalents across providers, for example Azure Virtual Machines to Google Compute Engine, Azure App Service to Google App Engine, Azure SQL to Cloud SQL, Azure Blob Storage to Cloud Storage, Azure Service Bus to Cloud Pub Sub, Azure Cosmos DB to Cloud Firestore or Bigtable, and Azure Kubernetes Service to Google Kubernetes Engine. The description also covers operational considerations such as region and zone architecture, high availability and fault tolerance, autoscaling, monitoring and logging, cost and billing differences, service limits and quotas, infrastructure as code and deployment tooling, and multi cloud trade offs including latency, data egress costs, compliance, and vendor lock in. Interview questions typically ask candidates to translate architectures and patterns between platforms, justify service choices, and explain migration or interoperability strategies.
Cloud Platforms and Infrastructure
Comprehensive understanding of cloud computing platforms and core infrastructure concepts. Candidates should know service models including Infrastructure as a Service, Platform as a Service, and Software as a Service, and be familiar with major providers such as Amazon Web Services, Google Cloud Platform, and Microsoft Azure. Core technical knowledge includes compute models, storage systems, networking fundamentals such as domain name system and load balancing, virtual private networks and network segmentation, virtualization, containerization for example Docker, orchestration with Kubernetes, serverless architectures, and microservices. Candidates should be able to evaluate trade offs between managed services and self managed solutions with respect to cost, reliability, operational burden, scalability, performance, security, and vendor lock in, and reason about when to choose platform managed services versus building custom infrastructure. The topic also covers system design considerations for high availability and fault tolerance, capacity planning and autoscaling, monitoring and observability, deployment strategies, and operational practices such as infrastructure as code and continuous integration and continuous delivery. This knowledge is critical for backend engineers, site reliability engineers, and DevOps roles and is increasingly relevant across many engineering positions.