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.
Cloud Cost Optimization and Financial Operations
Covers strategies and organizational practices for minimizing and managing cloud and infrastructure spend while balancing performance, reliability, and business priorities. Candidates should understand cloud cost drivers such as compute, storage, data transfer, and managed services; pricing models including on demand pricing, reserved capacity commitments, savings plans, and interruptible or spot offerings; and engineering techniques that reduce spend such as rightsizing, autoscaling, storage tiering, caching, and workload placement. This topic also includes financial operations practices for continuous cost management and governance: resource tagging and cost allocation, budgeting and forecasting, chargeback and showback models, anomaly detection and alerting, cost reporting and dashboards, and processes to gate changes that affect spend. Interviewees should be able to estimate recurring costs and total cost of ownership, identify and quantify optimization opportunities, weigh trade offs between cost and business objectives, and describe tools and metrics used to monitor and communicate cost to stakeholders.
Infrastructure Scaling and Capacity Planning
Operational and infrastructure level planning to ensure systems meet current demand and projected growth. Topics include forecasting demand headroom planning and three to five year capacity roadmaps; autoscaling policies and metrics driven scaling using central processing unit memory and custom application metrics; load testing benchmarking and performance validation methodologies; cost modeling and right sizing in cloud environments and trade offs between managed services and self hosted solutions; designing non disruptive upgrade and migration strategies; multi region and availability zone deployment strategies and implications for data placement and latency; instrumentation and observability for capacity metrics; and mapping business growth projections into infrastructure acquisition and scaling decisions. Candidates should demonstrate how to translate requirements into capacity plans and how to validate assumptions with experiments and measurements.