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Log compactionensures that Kafka retains at least thelatest value per keyin a topic. Compaction happensin the backgroundand removes older records with the same keyin inactive log segments, not immediately.

From theKafka Documentation > Log Compaction:

“Kafka guarantees that thelast message for each keywill be retained in the log after compaction, even if earlier messages with the same key are deleted.”

So, D is correct. A is incorrect because compaction does not overwrite; it's a background process. B is incorrect—deletion is not immediate. C incorrectly suggests client-side hashmap behavior.

With acks=all, the leader waits formin.insync.replicasto acknowledge the message. Since min.insync.replicas=3, Kafka will only commit the messageonce 3 brokers (leader + 2 followers)confirm they have the message.

FromKafka Documentation > Acks and Durability:

“If acks=all is specified, the producer will wait until the full set of in-sync replicas has acknowledged the record. The minimum number of in-sync replicas is controlled by min.insync.replicas.”

Even though the replication factor is 4, only3 acknowledgments are needed, as defined by min.insync.replicas.

To reset offsets for anexisting consumer group, you must use thekafka-consumer-groups.shtool with the --reset-offsets and --to-earliest flags.

FromKafka Consumer Group Tool Documentation:

“You can use the kafka-consumer-groups tool to reset offsets for a consumer group. This is required if the consumer has already committed offsets.”

Setting auto.offset.reset=earliest only worksif no committed offset exists.

Starting a new consumer with the same group won’t reset offsets.

Retention settings don’t affect committed offsets.

ForSASL/PLAIN authentication over SSL, the correct combination is:

security.protocol=SASL_SSL

sasl.jaas.config=...PlainLoginModule required username=... password=...

FromKafka Security Documentation:

“For SASL/PLAIN over SSL, configure security.protocol=SASL_SSL and provide login module configuration using sasl.jaas.config.”

Options B and D are incorrect because they do not pair the correct protocol with the authentication method.

BothLeaderNotAvailableExceptionandNotEnoughReplicasExceptionare subclasses of RetriableException, which representstransient issuesthat may succeed upon retry.

FromApache Kafka Java Client Documentation:

“RetriableException indicates that the request can be retried. This includes network errors and certain broker-side failures like leader not available or not enough replicas.”

RecordTooLargeException and AuthorizationException arenon-retriablebecause they represent client misconfigurations or access issues.

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.

These three metrics are critical for cluster health:

OfflinePartitionsCount: Indicates partitions without active leaders — a sign of broker failure.

ActiveControllerCount: There should beexactly one active controller. A count ≠ 1 signals controller failure.

UncleanLeaderElectionsPerSec: Tracks leader elections where out-of-sync replicas were selected — risky for data loss.

FromKafka Monitoring Documentation:

“Offline partitions and unclean leader elections should trigger alerts. Also, ensure a single active controller is running.”

A is about topics pending deletion — not critical.

E is a per-topic record metric, not broker-level.

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.

Using assign() givesmanual controlover partition assignment. In this mode:

B is true: The applicationchooses partitionsdirectly without involving the group coordinator.

C is true: Since no group coordination occurs,no rebalancinghappens if a consumer dies.

FromKafka Consumer API Docs:

“assign() manually assigns a list of partitions to this consumer. No coordination takes place and no rebalancing is triggered.”

A is false: subscribe() and assign() aremutually exclusive.

D is incorrect: Partitions don't need to be equal across topics.

Low I/O thread activity with blocked throughput often indicates thatproducer callbacks are consuming too much time, causing the sender thread to block while waiting for onCompletion() to finish.

FromKafka Producer Performance Guide:

“Expensive logic in callbacks (e.g., I/O or complex computation) can block the sender thread, reducing throughput.”

Compression (A) may slightly impact CPU but not I/O thread usage.

Large batches (B) improve throughput if managed correctly.

A Layer 2 network issue (C) would lead to packet loss, not specifically low callback metrics.