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About this interview
A technical interview on High-Level System Design: Data-Intensive and Streaming Systems, pitched at the hard level. A voice AI interviewer leads the conversation, adapts its questions to your answers, keeps you on topic, and afterward gives you honest, specific feedback on where you were strong and where to improve. Expect roughly 30 minutes.
What you'll be assessed on
Compare Lambda and Kappa architectures and explain when each is appropriate
Describe how Apache Kafka achieves durable, high-throughput ordered delivery and explain consumer group semantics
Design a distributed message queue from scratch covering replication, at-least-once delivery, and dead-letter queues
Explain backpressure, exactly-once semantics, and windowing strategies in stream processing
Topics covered
Requirements clarificationLambda architectureKafka fundamentalsKafka partitionsDelivery semanticsConsumer groupsPull vs pushReplication and durabilityStorage designKafka performanceKappa architectureLambda vs Kappa trade-offBackpressureWindowing
A few sample questions
Just examples to set expectations - the real interview has many more and adapts to your responses.
“When an interviewer says a system needs to handle 'high-throughput writes with low-latency reads,' what are the two or three most important questions you would ask before proposing any architecture?
“What are the three main windowing strategies in stream processing — tumbling, sliding, and session windows — and when would you pick each one?
“In a distributed message queue design, how would you implement exactly-once semantics end-to-end, covering both the producer side — preventing duplicate writes — and the consumer side — preventing duplicate processing?