Systems Architecture & Distributed Systems Topics
Large-scale distributed system design, service architecture, microservices patterns, global distribution strategies, scalability, and fault tolerance at the service/application layer. Covers microservices decomposition, caching strategies, API design, eventual consistency, multi-region systems, and architectural resilience patterns. Excludes storage and database optimization (see Database Engineering & Data Systems), data pipeline infrastructure (see Data Engineering & Analytics Infrastructure), and infrastructure platform design (see Cloud & Infrastructure).
Trade Off Analysis and Decision Frameworks
Covers the practice of structured trade off evaluation and repeatable decision processes across product and technical domains. Topics include enumerating alternatives, defining evaluation criteria such as cost risk time to market and user impact, building scoring matrices and weighted models, running sensitivity or scenario analysis, documenting assumptions, surfacing constraints, and communicating clear recommendations with mitigation plans. Interviewers will assess the candidate's ability to justify choices logically, quantify impacts when possible, and explain governance or escalation mechanisms used to make consistent decisions.
Architecture and Technical Trade Offs
Centers on system and solution design decisions and the trade offs inherent in architecture choices. Candidates should be able to identify alternatives, clarify constraints such as scale cost and team capability, and articulate trade offs like consistency versus availability, latency versus throughput, simplicity versus extensibility, monolith versus microservices, synchronous versus asynchronous patterns, database selection, caching strategies, and operational complexity. This topic covers methods for quantifying or qualitatively evaluating impacts, prototyping and measuring performance, planning incremental migrations, documenting decisions, and proposing mitigation and monitoring plans to manage risk and maintainability.
Trade-Off Analysis and Justification
Ability to identify key nonfunctional requirements and constraints and to compare alternative designs with clear, quantitative reasoning. Expect discussion of consistency versus availability, latency versus throughput, cost versus performance, operational complexity, and implementation risk. Candidates should demonstrate how to quantify trade offs using metrics such as latency percentiles, throughput, cost per request, and availability targets, how to choose appropriate consistency models and failure modes, and how to document and justify the selected architecture given product and business priorities.
Deep Dive into Complex System or Project
Being prepared to discuss any significant system or project from your background in detail. Be ready for followup questions testing depth of understanding. Interviewers will probe: What were the constraints? How did you make key decisions? What would you do differently? What surprised you? This validates that your understanding is genuine, not just surface-level.
Scalability and Performance Constraints
Evaluate knowledge of how systems scale and where performance constraints emerge. Topics include identifying bottlenecks, capacity planning, load balancing, caching and sharding strategies, performance testing and benchmarking, latency and throughput trade offs, and the effect of architectural decisions on cost and user experience. Assess ability to propose practical mitigations and to reason about performance trade offs in design and operational contexts.
Operational Scalability and Load Management
Focus on how design decisions affect system scalability, operational load, and long term maintainability. Candidates should articulate considerations such as real time versus batch updates, polling frequency, caching and throttling, and the impact of those choices on backend cost, latency, and reliability. Discuss design strategies that reduce support burden such as simpler workflows, clearer error states, automation of repetitive tasks, and progressive disclosure to limit edge case complexity. Describe approaches for graceful degradation, feature gating and progressive rollouts, monitoring and telemetry to measure operational impact, and how to collaborate with engineering and operations to quantify and mitigate load risks.
Decision Making Under Uncertainty
Focuses on frameworks, heuristics, and judgment used to make timely, defensible choices when information is incomplete, conflicting, or evolving. Topics include diagnosing unknowns, defining decision criteria, weighing probabilities and impacts, expected value and cost benefit thinking, setting contingency and rollback triggers, risk tolerance and mitigation, and communicating uncertainty to stakeholders. This area also covers when to prototype or run experiments versus making an operational decision, how to escalate appropriately, trade off analysis under time pressure, and the ways senior candidates incorporate strategic considerations and organizational constraints into choices.