Caching and Performance Optimization Questions

Covers design and implementation of multi layer caching and end to end performance strategies for web and backend systems. Topics include client side techniques such as browser caching, service worker strategies, code splitting, and lazy loading for components images and data; edge and distribution techniques such as content delivery network design and caching of static assets; and server side and data layer caching using in memory stores such as Redis and Memcached, query result caching, and database caching patterns. Includes cache invalidation and coherence strategies such as time to live, least recently used eviction, cache aside, write through and write behind, and prevention of cache stampedes. Covers when to introduce caching and when not to, performance and consistency trade offs, connection pooling, monitoring and metrics, establishing performance budgets, and operational considerations such as cache warm up and invalidation during deploys. Also addresses higher level concerns including search engine optimization implications and server side rendering trade offs, and how performance decisions map to user experience and business metrics at senior levels.

HardTechnical

29 practiced

Your company wants to roll out caching across multiple backend services to reduce DB costs, but leaders worry about correctness risk. Prepare a short plan (technical + rollout) describing how you'd pilot caching, measure success, handle invalidation, implement circuit-breakers, and provide rollback. Include what success metrics you'd track and how to mitigate data-staleness issues.

EasyBehavioral

33 practiced

Tell me about a time when adding caching caused a correctness or user-experience problem in a system you worked on. Describe the situation (Situation), what you were trying to achieve (Task), the actions you took (Action), and the outcome (Result). Focus on how you diagnosed the issue and the mitigation you implemented.

MediumTechnical

27 practiced

You operate a Redis cluster. Describe how you would size memory, tune maxmemory-policy, and manage memory fragmentation for a workload with lots of short-lived keys and some large value objects. Explain trade-offs between volatile-ttl, allkeys-lru, and noeviction policies and their impact on application behavior.

MediumTechnical

28 practiced

Design an observability/telemetry stack specifically for caching: what logs, metrics, traces and sampling strategy would you collect? Include examples of key dashboard panels (hit/miss heatmap, per-key latency percentiles, eviction trends) and how you'd use traces to root-cause tail latency that appears in cached requests.

HardSystem Design

37 practiced

GraphQL APIs often return deeply nested, personalized payloads. Design a caching approach for a GraphQL server that maximizes reuse: describe persisted queries, response fingerprinting, partial-field caching, query whitelisting, cache key strategies (root-level vs field-level), and how you'd handle authorization (Vary behavior).

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