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Increasing partitions increasesparallelism, but also means:

Consumers in a group may have to handlemore partitions, especially if the number of consumers is lower than the number of partitions.

This can result inincreased lag, especially under high load.

FromKafka Topic Management Docs:

“Increasing the number of partitions increases consumer work, and if consumers can’t keep up, lag can accumulate.”

A is false:existing data is not redistributed.

B is false:records with the same key always map to the same partitionbased on hash.

D is not directly impacted by the partition count.

fetch-rate: Measures how frequently consumers fetch data — critical for monitoring throughput.

incoming-byte-rate: Shows how much data is being written to the broker per second — a keyproducer-side metric.

FromKafka JMX Metrics Guide:

“fetch-rate, incoming-byte-rate, request-latency are key performance metrics for producers and consumers.”

B, C, E, and F areapplication-layer business metrics, not Kafka performance metrics.

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.

The offset in the RecordMetadata object returned by the producerrepresents the position of the record in the partition— i.e., thesequential IDassigned by Kafka once the message is committed.

FromKafka Producer API Documentation:

“The offset is the position of the record in the partition. This is a unique, sequential number assigned by the broker.”

D refers to metadata.partition(), not offset().

B and C are unrelated to how Kafka handles committed offsets.

To change the replication factor of an existing topic, you must perform apartition reassignment, because the replication factor is not directly modifiable via kafka-topics.sh. Thekafka-reassign-partitions.shtool is the official command-line utility to handle partition reassignments, including changing the replication factor.

This tool allows you to generate a reassignment JSON specifying new replica assignments per partition and then apply it.

From official documentation:

“To change the replication factor, use the kafka-reassign-partitions tool to assign the partitions to more or fewer brokers.”

Neither kafka-topics.sh nor kafka-reassign-topics.sh exists for this purpose, and kafka-recreate-topic.sh isnotan official Kafka tool.

If the application isCPU-boundandlagging, addingmore consumersto the group will allow betterparallel processing, especially since the topic has4 partitions, allowing up to 4 active consumers.

FromKafka Consumer Group Docs:

“Kafka achieves parallelism by distributing partitions across consumers in a group. Adding consumers helps reduce lag if partitions are underutilized.”

B may help but requires repartitioning and coordination.

C or D affects how much data is polled, not how fast it’s processed.

SMTs (Single Message Transforms) arelightweight transformationsapplied to individual messages as they pass through Kafka Connect.

Chaining SMTs: You can applymultiple SMTsin sequence by defining them in order in the connector config.

Masking or modifying fieldsis acommon use case(e.g., redacting sensitive data).

FromKafka Connect Documentation:

“Single Message Transforms (SMTs) are applied to individual messages. You can chain multiple SMTs together.”

SMTsdo not perform joins(B is incorrect).

Converters and SMTs areseparate concerns(D is incorrect).

The official CLI utility for consuming messages from Kafka topics is kafka-console-consumer.sh. It connects to the broker, consumes messages, and prints them to standard output.

FromKafka CLI Tools Documentation:

"kafka-console-consumer.sh is used to read data from a Kafka topic and write it to standard output."

The other options are not valid Kafka CLI tools.

Session windowsare ideal for trackingperiods of activity separated by inactivity, such as user sessions.

FromKafka Streams Documentation > Windowing:

“A session window captures streams of events that areintermittentand separated by agap of inactivity.”

Tumbling/Hopping/Sliding windows are fixed in size

Session windows are dynamic and close after inactivity timeout

This makes them perfect for identifyinggaps in user interaction.

unclean.leader.election.enable=false→ Prevents data loss by only considering in-sync replicas when rebalancing.

replication.factor→ Specifies how many redundant copies of partitions are distributed across brokers.

min.insync.replicas=2→ Sets the standard for the number of partition instances that must keep up with the latest committed message.

unclean.leader.election.enable=false ensures that onlyin-sync replicascan be elected as leaders. If disabled, an out-of-sync replica may become leader, potentially leading to data loss.

replication.factor defineshow many brokerswill maintain copies of each partition, directly impacting durability and availability.

min.insync.replicas determineshow many replicas must acknowledgea write when acks=all is used, enforcing write durability.