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.
Managed Services and Self Managed Tradeoffs
Compare managed cloud services with self managed alternatives and explain when to choose each based on scale cost operational burden and required control. Cover operational ownership upgrade and patch cadences security responsibilities portability and vendor lock in risk performance characteristics and debugging and observability differences. Use concrete workload examples such as managed databases managed event services serverless compute and self hosted containers or virtual machines to justify decisions and to surface long term operational and migration implications.
Cloud Strategy and Migration Planning
Fundamentals and planning practices for adopting cloud computing and migrating workloads to cloud environments. Coverage includes understanding cloud delivery models such as Infrastructure as a Service, Platform as a Service, and Software as a Service, and hybrid deployment options. Candidates should be able to evaluate migration strategies including lift and shift, refactor, replatform, and rebuild, and assess trade offs across cost, performance, security, compliance, and organizational readiness. Planning topics include workload assessment, suitability analysis, vendor evaluation for examples like Amazon Web Services, Microsoft Azure, and Google Cloud Platform, migration sequencing and runbooks, data migration and networking considerations, identity and access patterns, testing and rollback strategies, monitoring and observability, cost optimization and governance, and stakeholder and change management during migration.
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.
Cloud Service and Deployment Models
Comprehensive coverage of the primary cloud service and deployment choices and the decision criteria behind them. Explain Infrastructure as a Service, Platform as a Service, and Software as a Service, including responsibility boundaries, operational implications for security, patching, and management. Explain public cloud, private cloud, and hybrid cloud deployment types and the trade offs in control, isolation, scalability, cost, and compliance. Describe decision factors and example scenarios for selecting a service model or deployment type based on application characteristics such as multi tenancy, latency requirements, data sensitivity, regulatory constraints, integration with on premise systems, and team skills. Discuss vendor lock in, portability and migration implications, and how these choices affect cost, operational burden, resilience, and monitoring.
Compute and Cloud Native Architecture
Covers high level compute architecture decisions when building cloud native systems, including how microservices, containerization, and orchestration fit together. Topics include cloud native principles, serverless versus container trade offs, how containerized workloads change application and infrastructure design, deployability considerations, observability and telemetry, operational concerns when running services in managed cloud environments, and patterns for designing systems for independent scalability and fault isolation.
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.
Observability and Monitoring Architecture
Designing and architecting end to end observability and monitoring systems that scale, remain reliable under load, and do not become single points of failure. Topics include deciding which telemetry to collect and why including metrics logs traces and events, instrumentation strategies, collection models such as push versus pull, high throughput telemetry ingestion and pipeline design, time series storage and compression, aggregation and partitioning strategies, metric cardinality and retention tradeoffs, distributed tracing propagation and sampling strategies, log aggregation and secure storage, selection of storage backends and time series databases, storage tiering and cost optimization, query and dashboard performance considerations, access control and multi tenancy, integration with deployment pipelines and tooling, and design patterns for self healing telemetry pipelines. Senior level assessments include designing scalable ingestion and aggregation architectures, storage tiering and query performance optimization, cost and operational tradeoffs, and organizational impacts of observability data.
Analyzing Requirements and Service Selection
Given a business requirement (e.g., 'store real-time game data with sub-millisecond latency'), systematically identify appropriate cloud services and justify your choice based on performance, cost, and operational considerations. Articulate trade-offs explicitly.