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.
Capacity Planning and Resource Optimization
Covers forecasting, provisioning, and operating compute, memory, storage, and network resources efficiently to meet demand and service level objectives. Key skills include monitoring resource utilization metrics such as central processing unit usage, memory consumption, storage input and output and network throughput; analyzing historical trends and workload patterns to predict future demand; and planning capacity additions, safety margins, and buffer sizing. Candidates should understand vertical versus horizontal scaling, autoscaling policy design and cooldowns, right sizing instances or containers, workload placement and isolation, load balancing algorithms, and use of spot or preemptible capacity for interruptible workloads. Practical topics include storage planning and archival strategies, database memory tuning and buffer sizing, batching and off peak processing, model compression and inference optimization for machine learning workloads, alerts and dashboards, stress and validation testing of planned changes, and methods to measure that capacity decisions meet both performance and cost objectives.
Cloud Migration Strategy and Cutover
Plan end to end migration using common patterns such as rehost replatform refactor and repurchase and map those choices to workload characteristics. Define sequencing and phase gates plan pilot migrations and develop detailed cutover playbooks that include data replication validation reconciliation and rollback triggers. Address zero downtime techniques such as parallel running incremental synchronization canary deployments and feature gates plus testing strategies metrics to detect regressions and run books for rollback and post cutover verification.
Large-Scale Consumer Device Infrastructure and Operations
Assess a candidate's understanding of the scale, constraints, and distinctive operational challenges of running infrastructure for a company that manufactures and ships large volumes of consumer hardware devices, from managing hundreds of millions to billions of device identities to building a global data center and edge footprint that keeps those devices online and updated. Areas to address include privacy first architecture and telemetry minimization, staged device update and distribution rollouts, secure device attestation and key management, zero trust segmentation and defense in depth, and capacity planning for product launch spikes and other sudden demand surges. Interviewers expect references to concrete infrastructure trade offs and design patterns such as regional edge caching, multi region replication and consistency choices, canary and staged rollout strategies for firmware or software updates, and operational practices for safe change rollout and incident response when a defect or outage can affect millions of devices simultaneously. The assessment evaluates systems thinking across hardware and software boundaries, cross functional collaboration, and the ability to propose scalable, privacy aware, resilient designs that hold up at consumer device scale.
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.
Vendor Selection and Technology Evaluation
Approach for evaluating vendors, tools, and technology options before committing to them. Topics include defining requirements and success criteria, creating structured evaluation and scoring criteria, running proof of concept or pilot exercises, benchmarking performance and scalability, assessing total cost of ownership and ongoing operational burden, reviewing service level agreements and support/contract terms, examining vendor roadmaps and interoperability with existing systems, and planning for migration paths and vendor lock in mitigation. Applies to any technology or service selection decision (infrastructure and network hardware, cloud platforms, data and software tooling, security products, SaaS vendors, and similar), not just one domain.