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About this interview
A technical interview on Streaming and Messaging, pitched at the medium 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
Explain Kafka's architecture: brokers, topics, partitions, consumer groups, and offset management
Distinguish at-most-once, at-least-once, and exactly-once delivery semantics and their trade-offs
Describe when to choose stream processing over batch processing for a given use case
Explain how schema management tools (Avro, Schema Registry) prevent breaking changes in streaming pipelines
Topics covered
Stream vs BatchKafka FundamentalsKafka ArchitectureConsumer GroupsOffset ManagementKafka ReliabilityDelivery SemanticsStreaming Architecture PatternsKafka ConfigurationKafka ObservabilitySchema ManagementStream Processing ConceptsApache FlinkStream Processing Frameworks
A few sample questions
Just examples to set expectations - the real interview has many more and adapts to your responses.
“Can you explain what stream processing is and how it differs from batch processing at a fundamental level?
“What is the Lambda architecture? What problems does it solve, and what criticisms does it receive?
“How does Spark Structured Streaming's micro-batch model differ from Flink's true streaming model, and what are the practical implications for latency and exactly-once guarantees?