Scaling Fundamentals and Concepts Questions

Core concepts required to reason about scaling decisions and to communicate clear approaches. Topics include the difference between vertical and horizontal scaling and their trade offs; stateless versus stateful service design and why statelessness enables horizontal scaling; basic load balancing and request distribution strategies; when and how to apply caching replication and partitioning; simple autoscaling concepts and common metrics used to trigger scaling; how to identify common bottlenecks and apply pragmatic mitigations; and fundamental trade offs between latency throughput cost and complexity. This topic tests conceptual clarity and the ability to map requirements to simple scaling approaches.

HardTechnical

0 practiced

Design a tiered caching architecture consisting of CDN edge, regional caches, and local in-process caches for a mixed workload. Explain how autoscalers should react to cache hit ratio changes, how to handle cache warm-up on scale events, and when to scale compute versus cache memory to minimize cost while meeting latency SLAs.

EasyTechnical

0 practiced

In plain SRE terms, explain caching, replication and partitioning (sharding). For each technique give a short example use-case, the main trade-offs (consistency, memory/cost, complexity), and a one-line rule-of-thumb for when to apply it in a service architecture.

HardTechnical

0 practiced

Design a multi-layered autoscaling strategy for a critical, unpredictable service: include reactive HPA-style scaling, predictive/scheduled scaling (time-of-day or ML forecasts), and pre-warmed capacity. Specify metrics, prediction horizons, rollback mechanisms for prediction failures, and how to measure effectiveness.

HardTechnical

0 practiced

You're responsible for allocating SLOs and error budgets across 10 interacting services for one product. Describe a framework to assign SLOs per service, allocate error budgets, define automated actions when budgets are exhausted, and how to communicate trade-offs and priorities with product and engineering leadership.

MediumTechnical

0 practiced

An API returns user dashboards composed of heavy computed widgets and shared components (header, nav). Describe a hybrid caching strategy to reduce backend load while keeping personalized widgets fresh: include cache key design, TTLs, stale-while-revalidate, partial response caching, and invalidation considerations.

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