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Kafka Training — Topic-Wise Mock Tests
Choose any syllabus topic below to start a live timed MCQ exam (max 2 attempts per topic per day). Want structured live classes with a seasoned Industry Expert, all 244 full-length Mock Test sets, real module projects, Live Client Production deployment & Official Internship Letter? Enroll in the complete cohort for just ₹5,999!
All Modules & Topics in Kafka Training
Apache Kafka Fundamentals — Immutable Commit Log, Sequential Disk I/O & Zero-Copy `sendfile()`
Purpose: To understand and apply Apache Kafka Fundamentals — Immutable Commit Log, Sequential Disk I/O & Zero-Copy `sendfile()` in real Kafka systems — specifically: Core abstractions of events (key, value, timestamp, headers), why sequential append-only disk writes rival RAM speeds via OS PageCache, and how multiple independent consumers replay the same log, so your code stays reliable, fast, and easy to maintain in production.
Apache Kafka Architecture — Brokers, KRaft Controller Quorum, Partitions & `__cluster_metadata`
Purpose: To understand and apply Apache Kafka Architecture — Brokers, KRaft Controller Quorum, Partitions & `__cluster_metadata` in real Kafka systems — specifically: Inside a modern ZooKeeper-less Kafka cluster: KRaft Raft consensus voters, active controller metadata propagation, broker request/I/O thread pools, and partition segment files (`.log`, `.index`, `.timeindex`), so your code stays reliable, fast, and easy to maintain in production.
Apache Kafka Use Cases — Transactional Outbox, Event Sourcing, CQRS & Real-Time Telemetry
Purpose: To understand and apply Apache Kafka Use Cases — Transactional Outbox, Event Sourcing, CQRS & Real-Time Telemetry in real Kafka systems — specifically: Architectural patterns where Kafka shines (decoupled microservice event buses, Outbox + CDC avoiding dual writes, IoT telemetry ingestion, fraud stream scoring) and anti-use-cases (millions of ephemeral per-user topics), so your code stays reliable, fast, and easy to maintain in production.
Installing & Bootstrapping Apache Kafka in KRaft Mode (`kafka-storage.sh` & Listener Security)
Purpose: To understand and apply Installing & Bootstrapping Apache Kafka in KRaft Mode (`kafka-storage.sh` & Listener Security) in real Kafka systems — specifically: Step-by-step KRaft setup: generating a cluster UUID, formatting `log.dirs` (`meta.properties`), understanding `listeners` vs `advertised.listeners`, and tuning Linux `vm.swappiness=1` & `ulimit -n 100000`, so your code stays reliable, fast, and easy to maintain in production.
Kafka Topics Deep Dive — Partitions, Log Segments, Retention Policies & Key Compaction
Purpose: To understand and apply Kafka Topics Deep Dive — Partitions, Log Segments, Retention Policies & Key Compaction in real Kafka systems — specifically: How topics split into ordered immutable partitions, how active segments roll at `segment.bytes` / `segment.ms`, and how log compaction retains the latest value per key while purging keys marked with a `null`-payload tombstone, so your code stays reliable, fast, and easy to maintain in production.
Creating & Managing Kafka Topics (`kafka-topics.sh` & Idempotent Java `AdminClient`)
Purpose: To understand and apply Creating & Managing Kafka Topics (`kafka-topics.sh` & Idempotent Java `AdminClient`) in real Kafka systems — specifically: Creating, describing, and altering topics safely in production using `kafka-topics.sh --bootstrap-server` and `Admin.createTopics()`, plus why `auto.create.topics.enable=false` is mandatory in enterprise clusters, so your code stays reliable, fast, and easy to maintain in production.
Kafka Topic Replication — Leaders, Followers, ISR, High Watermark & Leader Epoch
Purpose: To understand and apply Kafka Topic Replication — Leaders, Followers, ISR, High Watermark & Leader Epoch in real Kafka systems — specifically: How follower brokers pull-replicate from partition leaders, when slow followers drop out of the In-Sync Replica (`ISR`) set (`replica.lag.time.max.ms`), why consumers only read up to the High Watermark (`HW`), and why Leader Epoch prevents log truncation bugs, so your code stays reliable, fast, and easy to maintain in production.
Apache Kafka Producer Architecture — Serializer, Partitioner, `RecordAccumulator` & Idempotence
Purpose: To understand and apply Apache Kafka Producer Architecture — Serializer, Partitioner, `RecordAccumulator` & Idempotence in real Kafka systems — specifically: Tracing `KafkaProducer.send()` across Interceptor -> Serializer -> Partitioner (Sticky Partitioner for keyless records!) -> `RecordAccumulator` `ProducerBatch` deque -> `Sender` network I/O thread, plus PID + Sequence Number idempotence, so your code stays reliable, fast, and easy to maintain in production.
Sending Data to Kafka Topics — CLI Key Parsing, Sync vs Async Delivery & `delivery.timeout.ms`
Purpose: To understand and apply Sending Data to Kafka Topics — CLI Key Parsing, Sync vs Async Delivery & `delivery.timeout.ms` in real Kafka systems — specifically: Using `kafka-console-producer.sh` with `--property parse.key=true --property key.separator=:`, understanding why calling `producer.send(rec).get()` on every message kills batching throughput by 100x, and bounding end-to-end latency with `delivery.timeout.ms`, so your code stays reliable, fast, and easy to maintain in production.
Creating a Kafka Producer in Java — Try-With-Resources, `StringSerializer` & Transactional Sends
Purpose: To understand and apply Creating a Kafka Producer in Java — Try-With-Resources, `StringSerializer` & Transactional Sends in real Kafka systems — specifically: Writing idiomatic Java 21 `KafkaProducer<String, String>` code with explicit `StringSerializer` classes, deterministic keys, atomic multi-partition transactions (`transactional.id`, `initTransactions`, `commitTransaction`), and clean resource closure, so your code stays reliable, fast, and easy to maintain in production.
Kafka Producer Callbacks — `RecordMetadata`, Retriable vs Non-Retriable Errors & I/O Thread Safety
Purpose: To understand and apply Kafka Producer Callbacks — `RecordMetadata`, Retriable vs Non-Retriable Errors & I/O Thread Safety in real Kafka systems — specifically: Inspecting partition, offset, and timestamp via `RecordMetadata` inside `Callback`, distinguishing retriable errors (`LeaderNotAvailableException`) from fatal ones (`RecordTooLargeException`), and keeping callbacks non-blocking, so your code stays reliable, fast, and easy to maintain in production.
Kafka Consumers & Consumer Groups — Partition Ownership, Heartbeats & `CooperativeStickyAssignor`
Purpose: To understand and apply Kafka Consumers & Consumer Groups — Partition Ownership, Heartbeats & `CooperativeStickyAssignor` in real Kafka systems — specifically: How `group.id` turns competing consumers into a scalable horizontal worker pool while distinct `group.id`s act as independent pub-sub subscribers, `GroupCoordinator` & `__consumer_offsets`, and `session.timeout.ms` vs `max.poll.interval.ms`, so your code stays reliable, fast, and easy to maintain in production.
Kafka Console Consumer — Key/Header Formatting, `--from-beginning` & `read_committed` Isolation
Purpose: To understand and apply Kafka Console Consumer — Key/Header Formatting, `--from-beginning` & `read_committed` Isolation in real Kafka systems — specifically: Inspecting live topics cleanly with `DefaultMessageFormatter` properties (`print.key=true`, `print.offset=true`, `print.timestamp=true`, `print.headers=true`), understanding `auto.offset.reset=earliest|latest`, and filtering aborted transactions via `--isolation-level read_committed`, so your code stays reliable, fast, and easy to maintain in production.
Kafka Consumer Group CLI — `LAG` Diagnostics, Hot-Key Skew & Safe `--reset-offsets --dry-run`
Purpose: To understand and apply Kafka Consumer Group CLI — `LAG` Diagnostics, Hot-Key Skew & Safe `--reset-offsets --dry-run` in real Kafka systems — specifically: Using `kafka-consumer-groups.sh --describe` to inspect `CURRENT-OFFSET`, `LOG-END-OFFSET`, `LAG`, and `CONSUMER-ID`, spotting hot-partition key skew when 1 partition lags by millions while 11 sit at `0`, and safely rewinding offsets, so your code stays reliable, fast, and easy to maintain in production.
Creating a Kafka Consumer in Java — Poll Loop, `enable.auto.commit=false` & `WakeupException`
Purpose: To understand and apply Creating a Kafka Consumer in Java — Poll Loop, `enable.auto.commit=false` & `WakeupException` in real Kafka systems — specifically: Why `KafkaConsumer` is deliberately **NOT thread-safe**, why `enable.auto.commit=true` loses messages if a worker crashes after `poll()` auto-commits an unprocessed batch, and how `commitAsync` + `onPartitionsRevoked` `commitSync` delivers clean at-least-once processing, so your code stays reliable, fast, and easy to maintain in production.
Multi-Cluster Kafka Architectures — MirrorMaker 2, Active-Active Replication & Stretch Clusters
Purpose: To understand and apply Multi-Cluster Kafka Architectures — MirrorMaker 2, Active-Active Replication & Stretch Clusters in real Kafka systems — specifically: Designing geo-distributed topologies: Hub-and-Spoke edge aggregation, Active-Passive DR, Active-Active bi-directional replication with MirrorMaker 2 topic prefixing (`us-east.orders`) to prevent infinite loops, and `RemoteClusterUtils` offset translation, so your code stays reliable, fast, and easy to maintain in production.
Kafka Connect & Debezium CDC — Distributed Workers, Internal Compacted Topics, SMTs & DLQ
Purpose: To understand and apply Kafka Connect & Debezium CDC — Distributed Workers, Internal Compacted Topics, SMTs & DLQ in real Kafka systems — specifically: Declarative Source & Sink connectors running in fault-tolerant Distributed Mode (`group.id`), the 3 compacted internal topics (`config.storage.topic`, `offset.storage.topic`, `status.storage.topic`), Debezium WAL/binlog Change Data Capture, Single Message Transforms (SMTs), and Dead Letter Queues, so your code stays reliable, fast, and easy to maintain in production.
Kafka Stream Processing — Embedded `KafkaStreams` Topologies, `exactly_once_v2` & `TopologyTestDriver`
Purpose: To understand and apply Kafka Stream Processing — Embedded `KafkaStreams` Topologies, `exactly_once_v2` & `TopologyTestDriver` in real Kafka systems — specifically: Why Kafka Streams runs as an embedded Java library inside standard microservices (zero separate Spark/Flink processing cluster required!), DAG stream topologies (`StreamsBuilder`), StreamTasks mapped 1:1 to input partitions, and `exactly_once_v2` atomic read-state-write commits, so your code stays reliable, fast, and easy to maintain in production.
Stream Processing Concepts — `KStream` vs `KTable` Duality, Event-Time Windows & RocksDB State
Purpose: To understand and apply Stream Processing Concepts — `KStream` vs `KTable` Duality, Event-Time Windows & RocksDB State in real Kafka systems — specifically: Stream-Table Duality (`KStream` unbounded facts vs `KTable` latest-value mutable state vs `GlobalKTable` full-replica dimension joins), Event Time vs Ingestion/Processing Time, Tumbling/Hopping/Sliding/Session windows with `.grace()`, and RocksDB off-heap state stores backed by compacted changelog topics, so your code stays reliable, fast, and easy to maintain in production.
Kafka Monitoring & SRE Observability — JMX Exporter, Critical Broker MBeans & Lag Alerting
Purpose: To understand and apply Kafka Monitoring & SRE Observability — JMX Exporter, Critical Broker MBeans & Lag Alerting in real Kafka systems — specifically: Exposing Kafka JMX MBeans to Prometheus & Grafana, monitoring P0 page-worthy alerts (`OfflinePartitionsCount > 0`, `ActiveControllerCount != 1`, sustained `UnderReplicatedPartitions > 0`), tracking `RequestHandlerAvgIdlePercent` / `NetworkProcessorAvgIdlePercent`, and time-based consumer lag SLAs, so your code stays reliable, fast, and easy to maintain in production.
KRaft Consensus Metadata Quorum (Zero ZooKeeper) & ISR Replication
Purpose: To understand and apply KRaft Consensus Metadata Quorum (Zero ZooKeeper) & ISR Replication in real Kafka systems — specifically: Understand leader election, high watermarks, and why acks=all + min.insync.replicas=2 prevents data loss, so your code stays reliable, fast, and easy to maintain in production.
Idempotent Producers (PID + Sequence Numbers) & EOS Transactions
Purpose: To understand and apply Idempotent Producers (PID + Sequence Numbers) & EOS Transactions in real Kafka systems — specifically: Eliminate duplicate messages during network retries and atomic consume-transform-produce flows, so your code stays reliable, fast, and easy to maintain in production.
Partition Key Sizing, Hot-Key Skew Mitigation & Log Compaction
Purpose: To understand and apply Partition Key Sizing, Hot-Key Skew Mitigation & Log Compaction in real Kafka systems — specifically: Balance throughput across brokers while preserving strict per-customer event order, so your code stays reliable, fast, and easy to maintain in production.
Batching (linger.ms, batch.size) & LZ4/Zstd Compression Tuning
Purpose: To understand and apply Batching (linger.ms, batch.size) & LZ4/Zstd Compression Tuning in real Kafka systems — specifically: Multiply Kafka throughput 5x while slashing cross-AZ network bandwidth bills, so your code stays reliable, fast, and easy to maintain in production.
CooperativeStickyAssignor vs Stop-the-World Eager Rebalances
Purpose: To understand and apply CooperativeStickyAssignor vs Stop-the-World Eager Rebalances in real Kafka systems — specifically: Scale consumer pods up and down without freezing event processing across all partitions, so your code stays reliable, fast, and easy to maintain in production.
Manual Offset Commits, At-Least-Once Idempotency & Poison-Pill Isolation
Purpose: To understand and apply Manual Offset Commits, At-Least-Once Idempotency & Poison-Pill Isolation in real Kafka systems — specifically: Ensure offsets commit only after durable DB writes and route bad payloads to DLQs, so your code stays reliable, fast, and easy to maintain in production.
Non-Blocking Retry Topics (retry-5s -> retry-1m -> DLT)
Purpose: To understand and apply Non-Blocking Retry Topics (retry-5s -> retry-1m -> DLT) in real Kafka systems — specifically: Retry transient downstream API timeouts without blocking healthy partition traffic, so your code stays reliable, fast, and easy to maintain in production.
Confluent/Apicurio Schema Registry (Avro/Protobuf) Compatibility Modes
Purpose: To understand and apply Confluent/Apicurio Schema Registry (Avro/Protobuf) Compatibility Modes in real Kafka systems — specifically: Enforce BACKWARD/FULL schema rules in CI so producer deploys never crash consumers, so your code stays reliable, fast, and easy to maintain in production.
Kafka Connect Distributed Workers, Source/Sink Connectors & SMTs
Purpose: To understand and apply Kafka Connect Distributed Workers, Source/Sink Connectors & SMTs in real Kafka systems — specifically: Stream data between databases, S3, and Elasticsearch with config instead of custom glue code, so your code stays reliable, fast, and easy to maintain in production.
Log-Based Debezium CDC (WAL/Binlog) vs Polling Anti-Patterns
Purpose: To understand and apply Log-Based Debezium CDC (WAL/Binlog) vs Polling Anti-Patterns in real Kafka systems — specifically: Capture every row insert/update/delete in milliseconds with near-zero primary DB load, so your code stays reliable, fast, and easy to maintain in production.
Transactional Outbox Event Router Pattern
Purpose: To understand and apply Transactional Outbox Event Router Pattern in real Kafka systems — specifically: Publish clean domain events atomically alongside microservice SQL commits, so your code stays reliable, fast, and easy to maintain in production.
Connector Secret Providers, Dead-Letter Sink Routing & Offset Resets
Purpose: To understand and apply Connector Secret Providers, Dead-Letter Sink Routing & Offset Resets in real Kafka systems — specifically: Operate Connect clusters securely and recover broken connectors cleanly, so your code stays reliable, fast, and easy to maintain in production.
KStream vs KTable Duality, GlobalKTable & Stream-Table Joins
Purpose: To understand and apply KStream vs KTable Duality, GlobalKTable & Stream-Table Joins in real Kafka systems — specifically: Enrich high-velocity click/payment events with customer tier metadata in real time, so your code stays reliable, fast, and easy to maintain in production.
Tumbling, Hopping & Session Windows + Watermarks for Late Events
Purpose: To understand and apply Tumbling, Hopping & Session Windows + Watermarks for Late Events in real Kafka systems — specifically: Detect 5-failed-card-attempts-in-2-minutes fraud bursts accurately even on delayed mobile events, so your code stays reliable, fast, and easy to maintain in production.
RocksDB State Stores, Changelog Topics & Standby Replicas
Purpose: To understand and apply RocksDB State Stores, Changelog Topics & Standby Replicas in real Kafka systems — specifically: Fail over stateful stream processors in seconds instead of replaying hours of state, so your code stays reliable, fast, and easy to maintain in production.
Consumer Lag Alerting, Cruise Control Rebalancing, mTLS/SASL & MirrorMaker 2
Purpose: To understand and apply Consumer Lag Alerting, Cruise Control Rebalancing, mTLS/SASL & MirrorMaker 2 in real Kafka systems — specifically: Monitor consumer lag SLAs, enforce ACL security, and replicate cross-region DR clusters, so your code stays reliable, fast, and easy to maintain in production.
Schema Registry Evolution Rules (Backward, Forward & Full Compatibility)
Purpose: To understand and apply Schema Registry Evolution Rules (Backward, Forward & Full Compatibility) in real Kafka systems — specifically: Evolve event payloads across teams without breaking downstream Kafka consumers in production, so your code stays reliable, fast, and easy to maintain in production.
Non-Blocking Retry Topics, Dead-Letter Topics (DLT) & Idempotent Consumer Deduplication
Purpose: To understand and apply Non-Blocking Retry Topics, Dead-Letter Topics (DLT) & Idempotent Consumer Deduplication in real Kafka systems — specifically: Isolate malformed or transiently failing records without stalling partition consumption, so your code stays reliable, fast, and easy to maintain in production.
Change Data Capture (CDC) with Debezium & Transactional Outbox Architecture
Purpose: To understand and apply Change Data Capture (CDC) with Debezium & Transactional Outbox Architecture in real Kafka systems — specifically: Stream committed database changes into Kafka reliably with zero dual-write race conditions, so your code stays reliable, fast, and easy to maintain in production.
CooperativeStickyAssignor, Static Group Membership & Preventing Rebalance Storms
Purpose: To understand and apply CooperativeStickyAssignor, Static Group Membership & Preventing Rebalance Storms in real Kafka systems — specifically: Keep high-throughput consumer groups stable during rolling pod deployments and GC pauses, so your code stays reliable, fast, and easy to maintain in production.
Monitoring Under-Replicated Partitions, ISR Shrinks, Disk Retention & Compaction
Purpose: To understand and apply Monitoring Under-Replicated Partitions, ISR Shrinks, Disk Retention & Compaction in real Kafka systems — specifically: Detect broker bottlenecks early and tune min.insync.replicas + acks=all for zero data loss, so your code stays reliable, fast, and easy to maintain in production.
Multi-Region MirrorMaker 2 & Event-Driven Architecture Interviews
Purpose: To understand and apply Multi-Region MirrorMaker 2 & Event-Driven Architecture Interviews in real Kafka systems — specifically: Design ordering guarantees, partition keys, and replayable event-sourced systems in interviews, so your code stays reliable, fast, and easy to maintain in production.