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.
Network Performance and Latency Optimization
Network level performance considerations including bandwidth, latency, jitter, packet loss, quality of service, congestion management, and capacity planning. Techniques covered include request batching, compression, connection pooling, content delivery networks, edge caching, and transport level tuning. Candidates should also discuss measurement and monitoring of network metrics, trade offs for global user bases, and strategies to optimize tail latency for latency sensitive services.
Cost Optimization for Mobile Services
Strategies to reduce both operational and user facing costs while maintaining acceptable user experience. Topics include minimizing network payloads and request frequency, efficient caching and cache invalidation, content delivery network strategies, client side compression and bundling, trade offs between offline support and server calls, right sizing backend compute and storage, pricing and quota considerations, telemetry for cost monitoring, and approaches to detect and mitigate cost anomalies.
Infrastructure Fundamentals
Foundational infrastructure and system components that underpin modern application architectures. Topics include relational and non relational database trade offs, application programming interfaces such as representational state transfer and remote procedure call frameworks, caching layers including Redis and Memcached, load balancers and their layer four and layer seven behaviors, message queues and asynchronous processing patterns, and containerization and orchestration technologies such as Docker and Kubernetes. Candidates should understand each component's purpose, how components interact in an end to end data flow, common failure modes and mitigation strategies, and operational concerns including deployment and rollback strategies, health checks, monitoring, logging, metrics and alerting. Important technical trade offs to reason about include latency and throughput implications, scalability patterns, consistency and durability properties, delivery semantics and idempotency, backpressure and retry strategies, dead letter queues, caching patterns and invalidation, and capacity planning and cost considerations. Interview questions typically probe component selection for given requirements, design choices to improve reliability and maintainability, and how these components fit together in real architectures.