Table of Contents

1. Apache Kafka (Architecture, Streams, Connect)
2. Redis Cache (Caching Strategies, Clustering, Pub/Sub)
3. Asynchronous Communication & Reactive Patterns
4. Messaging Queues (RabbitMQ, SQS, Kinesis, Kafka Comparisons)
5. Alerting, Monitoring & Observability
6. AWS Components & Cloud Integration
7. Conclusion

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🔹 Apache Kafka (Architecture, Streams, Connect)

1. What is Apache Kafka and what problems does it solve in distributed systems?
2. Explain Kafka’s core architecture: brokers, topics, partitions, leaders and followers.
3. How does Kafka achieve durability and high availability?
4. What is a partition key and how does partitioning affect ordering and scalability?
5. Explain the role of the Kafka controller.
6. How do producers and consumers work in Kafka? Describe producer acks and consumer groups.
7. What is the difference between consumer offset auto-commit and manual commit? When to use each?
8. Explain at-least-once, at-most-once, and exactly-once delivery semantics—how to implement them in Kafka?
9. What is idempotence for Kafka producers and how does it help with exactly-once semantics?
10. How does Kafka handle replication and leader election for partitions?
11. What is ISR (in-sync replica) and why is it important?
12. How do you tune Kafka producer and consumer performance (batch.size, linger.ms, fetch.min.bytes, max.poll.records)?
13. Explain log compaction vs log retention. When would you use each?
14. What is Kafka Streams and how is it different from Kafka Consumer API?
15. Explain the core concepts of Kafka Streams: KStream, KTable, state stores, and joins.
16. How do you implement windowed aggregations in Kafka Streams?
17. What is Kafka Connect? How does it simplify data integration?
18. Explain source vs sink connectors. Give examples for AWS integrations (S3, DynamoDB).
19. How do you ensure schema compatibility in Kafka? Discuss Schema Registry and Avro/JSON Schema/Protobuf.
20. How does Kafka handle backpressure and slow consumers? Strategies to mitigate.
21. How do you design topics and partitioning strategies for multi-tenant systems?
22. What are common Kafka monitoring metrics to track cluster health and performance?
23. How do you perform rebalancing and what are the pitfalls (stability, consumer downtime)?
24. How do you secure Kafka (TLS, SASL, ACLs) in production?
25. Describe a real-world example: designing an event-driven order processing pipeline with Kafka—what are the key design decisions?

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🔹 Redis Cache (Caching Strategies, Clustering, Pub/Sub)

26. What is Redis and where is it typically used in application architectures?
27. Explain the differences between in-memory cache (Redis) and distributed caches/databases.
28. What are common Redis data structures and real-world use cases for each (strings, hashes, lists, sets, sorted sets)?
29. What caching strategies exist (cache-aside, read-through, write-through, write-back)? Which to choose when?
30. How do you design a cache-aside pattern with Redis in a Spring Boot application?
31. How do you implement cache invalidation strategies? (time-based TTL, explicit invalidation, versioning)
32. How do you handle cache stampede, cache avalanche, and cache penetration?
33. Explain Redis persistence options: RDB snapshots and AOF. Trade-offs between them.
34. What are Redis eviction policies (LRU, LFU, volatile-*) and when to use each?
35. How do you configure and use Redis clustering for high availability and horizontal scaling?
36. Explain Redis replication (master-replica) and failover behavior.
37. How does Redis Sentinel work and when should you use it?
38. What is Redis Cluster hash slot allocation and how does it influence resharding?
39. How do you implement distributed locks using Redis (SETNX, Redlock)? Discuss safety and correctness.
40. When to use Redis Streams vs Kafka for event streaming?
41. How do you use Redis Pub/Sub? What are its limitations for production messaging?
42. Explain Redis transactions (MULTI/EXEC) and optimistic locking with WATCH.
43. How do you secure Redis in the cloud (TLS, AUTH, VPC, security groups)?
44. How to use Redis as a session store for stateless applications? Discuss consistency and scalability.
45. How do you measure Redis performance and which metrics matter (OPS, latency, memory usage, evictions)?
46. How do you shard data manually vs using Redis Cluster?
47. Explain memory optimization techniques for Redis (compression, data modeling).
48. How do you handle large sorted sets or huge keyspace in Redis effectively?
49. How to integrate Redis with AWS: Elasticache vs self-managed Redis on EC2?
50. Describe a production scenario: using Redis for leaderboard and real-time counters—design and pitfalls.

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🔹 Asynchronous Communication & Reactive Patterns

51. What are the benefits and trade-offs of asynchronous communication versus synchronous communication?
52. Describe common async patterns: fire-and-forget, request-reply, event notification, and CQRS.
53. How do you design idempotent message handlers for async systems?
54. Explain callback-based async vs Future/Promise vs reactive streams (Backpressure).
55. How do Java CompletableFuture and Project Reactor (Flux/Mono) differ? When to use each?
56. What is reactive programming and the Reactive Streams specification?
57. How do you apply backpressure in reactive systems and why is it important?
58. How do you design end-to-end tracing for async flows (correlation IDs, span propagation)?
59. What patterns help handle partial failures in async pipelines (retry, circuit breaker, DLQ)?
60. How do you implement asynchronous request-reply over Kafka or messaging systems?
61. What are the challenges of debugging async applications and how to mitigate them?
62. How do you guarantee ordering in async systems where ordering matters?
63. How do you build reactive REST APIs with Spring WebFlux and when to prefer it over Spring MVC?
64. What are the implications of using non-blocking IO for database access?
65. How do you manage thread pools and schedulers in reactive applications?
66. Explain eventual consistency vs strong consistency in async architectures and trade-offs.
67. How do you design async workflows that involve multiple microservices and external systems?
68. How to implement idempotent retries and exponential backoff in asynchronous processing?
69. How do you coordinate long-running async transactions (Saga, compensation) in practical systems?
70. What are the best practices for resource cleanup in async/reactive code?
71. How do you handle timeouts and cancellation in CompletableFuture and Reactor?
72. What role do message headers and metadata play in async messaging design?
73. How do you maintain SLA and observability for async endpoints?
74. Describe testing strategies for async code (unit tests, integration tests, virtual time).
75. How do you migrate blocking services to asynchronous/reactive paradigms—steps, pitfalls, and verification?

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🔹 Messaging Queues (RabbitMQ, SQS, Kinesis, Kafka Comparisons)

76. What are the core differences between Kafka, RabbitMQ, and AWS SQS?
77. When should you pick a message broker vs an event log (Kafka)?
78. Explain message durability, persistence, and acknowledgement semantics.
79. How do you design dead-letter queue (DLQ) strategies and poison message handling?
80. How can you ensure message ordering when using queues that do not guarantee it?
81. Discuss retry strategies: immediate retry, delayed retry, backoff, and retry count limits.
82. How do you ensure exactly-once processing semantics across queues and downstream systems?
83. What are transactional messaging patterns and how to implement them with JMS/Rabbit/SQS?
84. How do you handle large messages (payloads) efficiently with messaging systems?
85. How do you design idempotent consumers to tolerate duplicates?
86. Explain consumer scaling patterns (competing consumers, sharding, partitioning).
87. What is message routing and how do exchanges/types work in RabbitMQ?
88. How do you handle fanout and multicast messaging patterns?
89. How to implement request-reply over message queues?
90. What security measures apply to messaging systems (TLS, IAM, ACLs)?
91. How to integrate messaging systems with Spring Boot (Spring AMQP, Spring Cloud Stream)?
92. What is the role of connectors and bridges (Kafka Connect, SQS-to-Kafka) in hybrid landscapes?
93. How to manage schema and payload evolution in message-based systems?
94. How do you observe and instrument message throughput, latencies, and queue depth?
95. What strategies exist for batching and compression of messages for throughput?
96. How to design a transactional outbox pattern to ensure reliable message publishing?
97. How do you manage quotas and throttling for message producers/consumers?
98. How to design cross-region messaging and replication for disaster recovery?
99. What are the operational best practices for running managed brokers (MSK, RabbitMQ, SQS)?
100. Provide a practical design: order-to-fulfillment flow using SQS + Lambda + DynamoDB—outline components and failure modes.

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🔹 Alerting, Monitoring & Observability

101. What is the difference between monitoring, logging, tracing, and alerting?
102. Which metrics are most important to monitor for a messaging or streaming system?
103. How do you design SLOs/SLIs and alert thresholds for backend services?
104. How do you implement distributed tracing across services (OpenTelemetry, Jaeger, Zipkin)?
105. What is the role of correlation IDs and how do you propagate them across async boundaries?
106. How do you centralize logs and what makes a good logging strategy (structured logs, log levels)?
107. How do you avoid alert fatigue—designing meaningful alerts and runbooks?
108. How to monitor Kafka clusters: key metrics, alert conditions, and dashboards?
109. How to monitor Redis: memory usage, eviction rates, latency, and replication health?
110. How do you instrument application code for metrics (Micrometer, Prometheus client)?
111. How do you implement anomaly detection and alerting for unusual traffic patterns?
112. How to set up dashboards in Grafana to visualize system health and bottlenecks?
113. What logs/metrics are critical for diagnosing message loss or duplication?
114. How do you measure end-to-end latency for async workflows?
115. How do you implement health checks and readiness/liveness probes for containerized services?
116. How to use AWS CloudWatch for logs, metrics, and alerts—best practices?
117. How do you trace and debug issues that cross multiple async systems (Kafka → Lambda → DB)?
118. What are effective strategies for capacity planning based on observed metrics?
119. How do you design automated self-healing or remediation playbooks (auto-scaling, restart, failover)?
120. How do you secure monitoring pipelines to avoid leaking sensitive data?
121. What are the key considerations when monitoring serverless functions (cold starts, duration, errors)?
122. How to integrate logging, tracing and metrics for a single pane of glass observability?
123. How to create actionable incident reports post-mortem using observability data?
124. How do you test your alerting strategy (synthetic tests, chaos engineering)?
125. Describe a runbook for a Kafka partition under-replicated or offline event—steps to remediate.

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🔹 AWS Components & Cloud Integration

126. When designing cloud-native async systems, how do you choose between managed services (MSK, SQS, Kinesis) vs self-managed?
127. How do you integrate Kafka with AWS (MSK, EC2-based Kafka, Connect to S3)?
128. When to use AWS SQS vs SNS vs Kinesis for messaging and streaming?
129. How do you design an event-driven ETL pipeline using S3, Kinesis, Glue, and Lambda?
130. How do you configure IAM roles and policies for least-privilege access to messaging and storage services?
131. How do you use AWS Lambda with message triggers (SQS, SNS, Kinesis) and handle retries and DLQs?
132. How do you integrate DynamoDB streams with event processing and ensure exactly-once processing?
133. How do you architect cross-account or cross-region messaging securely on AWS?
134. How to use AWS Step Functions to orchestrate complex async workflows?
135. How do you store large messages or attachments in S3 while sending references via queues?
136. How do you implement streaming ETL from Kafka to S3 and Redshift using connectors or Glue?
137. How do you manage schema registry and compatibility when producing to Kafka topics in AWS?
138. How do you monitor AWS-managed services (MSK, Kinesis, SQS) and connect those metrics to Prometheus/Grafana?
139. How do you implement cross-service authentication and mTLS between services on AWS ECS/EKS?
140. How to design secure, scalable consumer groups for MSK and Kinesis with autoscaling?
141. How do you implement cost optimization strategies for messaging and streaming workloads on AWS?
142. How do you leverage AWS EventBridge vs SNS for event-driven architectures?
143. What are patterns for handling eventual consistency when using AWS managed databases (RDS, DynamoDB)?
144. How do you design disaster recovery and backup strategies for Kafka and Redis running on AWS?
145. How do you use CloudWatch Logs + OpenTelemetry to collect telemetry from serverless and containerized services?
146. How do you secure secrets for messaging clients and applications (Secrets Manager, Parameter Store)?
147. How do you implement CI/CD for infrastructure and messaging schema changes (Terraform + Schema Registry + Canary releases)?
148. How do you test failover and recovery in managed and self-managed broker setups on AWS?
149. How do you design multi-region, low-latency data pipelines using AWS global services?
150. Describe an architecture for high-throughput event ingestion on AWS using MSK → Lambda → DynamoDB → S3 for analytics.

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🏁 Conclusion

🔥 This professional, structured, and comprehensive 150+ question master list focuses on the critical backend topics—Apache Kafka, Redis caching, asynchronous communication patterns, messaging queues, alerting & monitoring, and AWS cloud integrations—designed for Senior Software Backend Engineers (10+ years experience).

• ✔️ Core distributed messaging (Kafka, MSK, Kinesis)
• ✔️ Caching strategies & operational considerations (Redis, ElastiCache)
• ✔️ Async/reactive design and robustness (CompletableFuture, Reactor, Sagas)
• ✔️ Messaging queue patterns and reliability (SQS, RabbitMQ, DLQs, outbox)
• ✔️ Observability, alerting, and incident response (Prometheus, Grafana, CloudWatch, tracing)
• ✔️ Secure and scalable AWS integrations and operational best practices

By studying and practicing these questions (conceptual explanations, real-world designs, troubleshooting, and hands-on exercises), you will be prepared to face interviews across startups, product companies, large enterprises, and cloud-native teams worldwide.