System Design and Scalability Questions

Covers architectural thinking and design tradeoffs for building reliable, high performance systems. Topics include design decision reasoning given constraints such as cost, latency and availability; scaling strategies including horizontal and vertical scaling, load balancing, caching patterns, database partitioning and sharding, read replicas, and asynchronous processing; capacity planning and observability; spotting and explaining bottlenecks such as hot partitions, single points of failure, database locks and network limits; and communicating technical impact in business terms. Candidates should be able to justify choices, compare alternatives, and articulate metrics and monitoring approaches to validate design decisions.

HardSystem Design

16 practiced

Design an SLO-driven observability and remediation system for a cloud service. Define how you'd set SLOs and error budgets, detect SLO breaches in real time, trigger automated remediation (scale-up, restart pods, rollback), and ensure safe human-in-the-loop escalation. Describe monitoring pipelines, runbook automation, and how you would measure remediation effectiveness while avoiding noisy or harmful automations.

EasyTechnical

23 practiced

Explain the circuit breaker pattern and exponential backoff strategies. As a Cloud Engineer, describe how to apply them in a microservices environment to protect downstream services from overload, what configuration parameters you would expose (error thresholds, timeout windows, half-open behavior), and how to monitor and alert based on circuit state.

HardSystem Design

17 practiced

Design a global shopping-cart service for an e-commerce site that must provide low-latency reads (sub-100ms), strong consistency for an individual user's cart across devices, and high availability across regions. Explain data model, replication strategy (single-primary per user versus multi-master), caching approach, conflict resolution, and cost/latency trade-offs.

EasyTechnical

24 practiced

Given a typical three-tier cloud application (Internet -> Load Balancer -> Web/API servers -> Database -> Cache), list potential single points of failure (SPOFs) at each layer in a typical AWS deployment. For each SPOF propose specific cloud-native mitigations including multi-AZ/multi-region deployment, managed services, failover automation, and testing strategies to validate resilience.

MediumTechnical

16 practiced

A new microservice will handle sensitive user data and sees unpredictable traffic. As a Cloud Engineer, compare using managed services and serverless (managed DB, serverless compute) versus self-managed containers in Kubernetes. Discuss security/compliance, operational overhead, cold-start and latency, scalability, and cost trade-offs, including long-term operational burden.

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