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Caching and Asynchronous Processing Questions

Design and operational patterns for reducing latency and decoupling components using caching layers and asynchronous communication. For caching, understand when to introduce caches, cache placement, eviction policies, cache coherence, cache invalidation strategies, read through and write through and write behind patterns, cache warming, and trade offs between consistency and freshness. For asynchronous processing and message driven systems, understand producer consumer and publish subscribe patterns, event streaming architectures, common brokers and systems such as Kafka, RabbitMQ, and Amazon Simple Queue Service, and the difference between queues and streams. Be able to reason about delivery semantics including at most once, at least once, and exactly once delivery, and mitigation techniques such as idempotency, deduplication, acknowledgements, retries, and dead letter queues. Know how to handle ordering, partitioning, consumer groups, batching, and throughput tuning. Cover reliability and operational concerns such as backpressure and flow control, rate limiting, monitoring and alerting, failure modes and retry strategies, eventual consistency and how to design for it, and when to choose synchronous versus asynchronous approaches to meet performance, scalability, and correctness goals.

HardTechnical
69 practiced
Explain how to achieve exactly-once semantics when using Kafka Streams to write updates to an external database. Discuss transactional producers/consumers, idempotent writes in the DB, the outbox pattern, and the performance and operational implications of each approach.
HardSystem Design
38 practiced
Architect a multi-region cache for a read-heavy social feed application with 100M users and 200k writes/sec peak. The system must serve reads from nearest region with low latency while ensuring users see their own recent writes within 1-2 seconds in most cases. Discuss active-active vs active-passive cache models, invalidation/replication strategies, conflict resolution, and user experience trade-offs.
EasyTechnical
54 practiced
Compare message queues and event streams. Explain differences in semantics (point-to-point vs pub/sub), retention and replayability, ordering guarantees, consumer groups, and typical use-cases for each (e.g., task queues vs event sourcing). Give a concrete example of when you would choose Kafka over SQS and vice versa.
EasyTechnical
41 practiced
Explain cache hit, miss, and eviction. Describe common metrics (hit rate, miss rate, eviction rate, average latency, time-to-first-byte) and how you would measure them in a production system. Given a cache that receives 100k requests/min and returns 80k hits, calculate the hit rate and discuss implications for capacity sizing and SLOs.
MediumTechnical
51 practiced
You inherit a system with multiple caches and message queues. Propose a monitoring and alerting plan focusing on cache and broker health: list key metrics (cache hit rate, miss latency, eviction rate, broker lag, consumer lag, throughput), alert thresholds, dashboards, and how to escalate incidents. Include suggestions for runbooks and automated remediation.

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