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.
Networking, VPC, and Connectivity
Deep understanding of AWS VPC architecture including subnets (public and private), route tables, Network Address Translation (NAT), internet gateways, and VPC endpoints. Knowledge of security groups and network ACLs. Understanding of VPN and AWS Direct Connect for hybrid connectivity. DNS and Route 53 routing policies.
Azure Services and Cloud Models
Fundamental understanding of cloud delivery models and the Microsoft Azure platform and services. Cover core cloud service models such as Infrastructure as a Service, Platform as a Service, and Software as a Service, trade offs in cost and operational burden, and mapping workloads to appropriate models. For Microsoft Azure, discuss compute and orchestration options such as virtual machines and Azure Kubernetes Service, platform services such as App Service, storage options such as Blob storage and file shares, database offerings such as Azure SQL Database and Cosmos DB, identity and access controls such as Azure Active Directory and managed identities, and managed platform capabilities for machine learning and artificial intelligence. Candidates should be able to explain service level trade offs, integration and networking basics for Azure, and how vendor direction influences architectural choices.
Infrastructure as Code Tools
Practical skills for authoring, deploying, and managing Infrastructure as Code templates and configurations across cloud platforms. Candidates should be able to author, read, and modify templates or configuration files for native platform tools such as AWS CloudFormation, Azure Resource Manager templates or Bicep, and Google Cloud Deployment Manager, as well as for multi cloud tools such as Terraform. Key areas include file formats such as YAML and JSON, declaring resources, passing parameters or variables, and emitting outputs, together with expressing resource dependencies, conditions, and mappings. Candidates should be able to write templates for common infrastructure patterns including networking such as virtual private clouds, subnets, and security groups, compute resources such as virtual machines and instances, and storage resources such as buckets and storage accounts. They should know how to deploy templates to create stacks or equivalent constructs, perform stack updates and change sets or plan and apply workflows, handle rollbacks and deletions, and manage state for tools that require it including remote state and state locking. Additional important skills are modularization through nested stacks or modules, template validation and linting, integration with continuous integration and continuous delivery pipelines, drift detection and remediation, and basic troubleshooting of template errors and deployment failures. Interview tasks may include writing or modifying short templates, explaining the lifecycle of a deployment, and comparing trade offs between native templates and multi cloud tooling.
Introduction to Major Cloud Providers
Have a basic overview of AWS, Azure, and GCP. Know that AWS leads market share and offers the broadest service portfolio; Azure has strong enterprise integration and hybrid capabilities; GCP excels in data analytics and machine learning. Know the naming conventions: AWS uses specific names (EC2, S3, RDS, Lambda); Azure has similar services with different names (Virtual Machines, Blob Storage, Azure SQL, Functions); GCP has its own naming (Compute Engine, Cloud Storage, Cloud SQL, Cloud Functions). Understand that while they offer similar core services, each has unique strengths and different learning curves.
Cloud Platform Experience
Personal account of hands on experience using public cloud providers and the concrete results delivered. Candidates should describe specific services and patterns they used for compute, storage, networking, managed databases, serverless and eventing, and explain their role in architecture decisions, deployments, automation and infrastructure as code practices, continuous integration and continuous delivery pipelines, container orchestration, scaling and performance tuning, monitoring and incident response, and cost management. Interviewees should quantify outcomes when possible with metrics such as latency reduction, cost savings, availability improvements or deployment frequency and note any formal training or certifications. This topic evaluates depth of practical experience, ownership, and the ability to operate and improve cloud systems in production.
Governance, Policy Enforcement, and Guardrails
Implementing policy as code, compliance checking, and safety mechanisms into infrastructure systems. Topics include automated cost controls, security policy enforcement, resource naming standards, tagging strategies, and preventing common misconfigurations. Discussion of balance between flexibility and governance.
Azure Compute Options and Trade Offs
Explain compute choices on Azure and the trade offs among them. Cover virtual machines for full operating system control, App Service for managed web hosting, Azure Functions for event driven serverless workloads, container instances for single container tasks, and Azure Kubernetes Service for orchestrated container platforms. For each option describe the operational responsibilities, scalability characteristics, cost model, deployment complexity, and suitability for stateful versus stateless workloads. Be prepared to justify a choice based on latency and performance needs, team expertise, deployment frequency, and cost constraints.
Azure Core Services Overview
Demonstrate familiarity with the primary categories of Azure services and the common offerings within each category. Explain compute options such as virtual machines, App Service, and Azure Functions; storage options such as blob storage, file shares, queue and table storage; networking constructs such as virtual networks and load balancers; and database offerings such as Azure SQL Database, Azure Database for PostgreSQL, and Cosmos DB. Also be familiar with security and identity tools like Azure Key Vault and role based access control, and with monitoring solutions such as Azure Monitor and Application Insights. For each category explain typical use cases, operational trade offs, and basic selection criteria based on control, scalability, cost, and maintenance burden.
Load Balancing and Horizontal Scaling
Covers principles and mechanisms for distributing traffic and scaling services horizontally. Includes load balancing algorithms such as round robin, least connections, and consistent hashing; health checks, connection draining, and sticky sessions; and session management strategies for stateless and stateful services. Explains when to scale horizontally versus vertically, capacity planning, and trade offs of each approach. Also includes infrastructure level autoscaling concepts such as auto scaling groups, launch templates, target tracking and step scaling policies, and how load balancers and autoscaling interact to absorb traffic spikes. Reviews different load balancer types and selection criteria, integration with service discovery, and operational concerns for maintaining availability and performance at scale.