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The default maximum message size that a Kafka broker accepts is1MB (1,048,576 bytes), controlled by the config propertymessage.max.bytes.

FromKafka Broker Configuration Docs:

“The default maximum message size is 1MB. To accept larger messages, configure message.max.bytes and the producer’s max.request.size.”

Producers also have a matching limit via max.request.size, and consumers via fetch.message.max.bytes.

In Kafka Streams,the number of stream tasks = number of input partitions × num.stream.threads × number of instances,but only as many as the number of partitions can actively consume at once.

However, in this case,Kafka Streams assigns one task per partition, and because there are 5 partitions and 15 threads (3 instances × 5 threads),15 tasks are created, andall 15 can be activedepending on processing topology.

FromKafka Streams Developer Guide:

"Kafka Streams creates one task per input partition. If you increase the number of stream threads, it runs multiple tasks in parallel within a single instance."

So,15 stream tasks will be created and 15 will be actively consuming.

TheStickyAssignortries to minimize partition movement bypreserving existing assignmentsas much as possible while still achieving a balanced assignment. This improvesconsumer stabilityand reduces rebalances.

From theKafka Consumer Assignor Documentation:

“The StickyAssignor attempts topreserve as many existing assignments as possible, which helps minimize partition movement between rebalances.”

RoundRobinAssignor focuses on even distribution, not stability.

RangeAssignor groups partitions by topic and assigns them consecutively, but can lead to imbalances.

PartitionAssignor is an abstract base class, not an assignor used directly.

Comprehensive and Detailed Explanation From Exact Extract:

✅C. Consumers can consume both committed and uncommitted transactions.By default,Kafka consumers only read committed messagesif they are configured with isolation.level=read_committed. However, if configured as read_uncommitted, theycan also consume uncommitted (potentially aborted) transactional messages.

From Kafka Documentation:

"The isolation.level setting controls whether the consumer will read only committed messages or all messages, including uncommitted messages from ongoing or aborted transactions."

✅D. Information about producers and their transactions is stored in the _transaction_state topic.Kafka uses an internal topic named__transaction_stateto maintain metadata about producer transactions. This topic is essential for tracking thetransaction lifecycle, fencing, and recovery.

From Kafka Internals:

“Kafka stores the state of active and completed transactions in an internal topic called __transaction_state.”

Kafka Connectsource connectorsuse theoffset.storage.topicconfiguration parameter to define where to storeoffsets that track the connector's positionin the source system (e.g., database, file, etc.).

FromKafka Connect Worker Configuration:

“offset.storage.topic specifies the topic in Kafka where the connector stores offsets for each source partition.”

This allows Kafka Connect toresumefrom the correct position on restart.

The other options (B–D) are not valid Kafka configs.

For Kafka Streams to performjoins, topics must beco-partitioned, meaning:

They have thesame number of partitions(A), and

They use thesame partitioning strategy(typically by key) (B)

FromKafka Streams Documentation > Joins:

“Co-partitioning is required for Kafka Streams joins. Topics must have the same number of partitions and use the same key-based partitioner.”

Value schema (C) and retention time (D) have no bearing on join compatibility.

AGlobalKTableis a replicated, read-only table available in full on all instances. It's best forreference or lookup datasetssuch as:

Product catalog

Customer profiles

Warehouse inventory

FromKafka Streams Developer Guide:

“Use GlobalKTable when you need to perform joins using non-partition-aligned reference data that's small enough to replicate.”

Purchases and transactions are high-throughput, time-sensitive streams, not static reference data.

Increasingtasks.maxallows the connector to createmultiple tasks, each responsible for processing different tables or table partitions. If onlyone taskis used,adding more workers has no effect.

FromKafka Connect JDBC Connector Docs:

“Use tasks.max to configure how many tasks are created for the connector. Each task can process a subset of the data.”

A and C help only if the bottleneck is in Kafka, not Connect.

D is a large architectural change, not the first step.

Kafka Streams usestransactional producersto guaranteeexactly-once semantics (EOS). This ensures that both theoutput recordsandstate store updatesare committed atomically, avoiding duplication or partial writes.

FromKafka Streams Documentation > Processing Guarantees:

“Kafka Streams leveragesKafka’s transactional APIsto commit the output records and internal state updatesas a single atomic unit, thereby providing exactly-once semantics.”

Option A is incorrect because log compaction is not disabled for EOS.

Option C incorrectly describes a checkpointing system Kafka Streams does not use.

Option D refers to deduplication, which is not how EOS is achieved in Streams.

max.poll.interval.ms defines themaximum delay between invocations of poll()before the consumer is considered failed. It essentially gives consumers more time to process records before needing to poll again.

FromKafka Consumer Configuration Reference:

“max.poll.interval.ms: The maximum delay between invocations of poll() when using consumer group management. If poll() is not called before expiration, the consumer is considered failed.”

session.timeout.ms and heartbeat.interval.ms relate to group coordination and heartbeats.

fetch.max.wait.ms affects how long the broker waits to accumulate data before sending a fetch response.