In ksqlDB, INSERT VALUES is used to produce data directly into a topic that underlies a STREAM or TABLE. It is a DML command that appends new records into a Kafka topic.

From ksqlDB documentation:

“INSERT VALUES adds data into a stream or table. This data is persisted in the Kafka topic backing the stream or table.”

Option analysis:

A/B → SELECT queries retrieve data but do not write.

D → Defines a table, does not write data.

C → Correct: Actually writes (produces) data to Kafka.

Page Reference:

Kafka: The Definitive Guide, 1st Edition, Chapter 7, p. 227–229

Confluent Docs: ksqlDB INSERT INTO and INSERT VALUES Statements

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Kafka Replicator does not preserve source cluster offsets. Offsets in the destination DR cluster are generated independently, and there is no guarantee they match those from the source cluster.

From Confluent Replicator documentation:

“Replicator does not guarantee preservation of message offsets between source and destination. Offsets in the destination cluster are newly assigned.”

C → Correct: Offsets may differ even for the same data.

A → Incorrect: Lag is not the issue if offsets inherently differ.

B → Misleading: Kafka does not overwrite committed messages.

D → Invalid: Kafka messages are immutable; no "update" happens.

Page Reference:

Kafka: The Definitive Guide, 1st Edition, Chapter 10, p. 313

Confluent Replicator Documentation: “Offset Mapping and Limitations”

Kafka topics support two main cleanup policies:

delete: The default policy where Kafka deletes old log segments based on retention time or size.

compact: Enables log compaction, retaining only the latest value for each key.

From Kafka documentation:

“Valid cleanup.policy values are 'delete' and 'compact'.”

B → Invalid: 'default' is not a recognized cleanup policy.

D → Invalid: 'cleanup' is not a valid keyword in this context.

Page Reference:

Kafka: The Definitive Guide, 1st Edition, Chapter 6, p. 196–197

Apache Kafka Documentation: Topic Configs – cleanup.policy

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Kafka supports multiple SASL mechanisms for authentication, including:

SASL/PLAIN – Username/password

SASL/GSSAPI – Kerberos-based

SASL/OAUTHBEARER – OAuth 2.0 token-based

From Apache Kafka documentation:

“Kafka supports the following SASL mechanisms: GSSAPI (Kerberos), PLAIN, SCRAM, OAUTHBEARER.”

B → SAML20 is not supported.

E → OTP is not a supported SASL mechanism.

Page Reference:

Kafka: The Definitive Guide, 1st Edition, Chapter 9, p. 280–284

Apache Kafka Documentation: Kafka SASL Authentication

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The Kafka REST Proxy, provided as part of Confluent Platform, allows clients to interact with Kafka over REST APIs. When securing communication between REST Proxy and REST clients, it uses TLS (HTTPS).

As per the official Confluent documentation for REST Proxy security:

“The REST Proxy supports HTTPS to encrypt communication between REST clients and the REST Proxy.”

TLS/SSL encryption ensures that data in transit is secure and private.

Other options listed, such as MD5 (a hashing algorithm), SCRAM (an authentication mechanism), and Kerberos (an authentication protocol), do not provide direct encryption of the communication channel itself for REST Proxy. They can be part of broader security configurations (e.g., SCRAM for Kafka brokers, Kerberos for SASL authentication), butTLS (HTTPS)is the explicit mechanism for encrypting data between the REST Proxy and its clients.

Page Reference:

Confluent Kafka: The Definitive Guide, 1st Edition, Chapter 9 (“Kafka Security”), Section “REST Proxy Security” (p. 288–289).

Confluent Platform REST Proxy Documentation, “Encryption (HTTPS)”.

The linger.ms property defines how long the producer will wait to fill up a batch before sending it. Combined with batch.size, it allows the producer to accumulate more messages, thus improving throughput.

From Kafka documentation:

“If batch.size is not yet reached, linger.ms causes the producer to wait for additional messages before sending the batch.”

A → send.buffer.bytes affects TCP socket size.

B → Correct: Improves batching by adding intentional delay.

C → Reduces bandwidth but not directly batching.

D → A timeout for message delivery, not related to batching.

Page Reference:

Kafka: The Definitive Guide, 1st Edition, Chapter 5, p. 162

Apache Kafka Documentation: Producer Config – linger.ms

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Kafka's Controller is a broker that also performs controller duties. It is not a dedicated node. Therefore, it continues to host topics, partitions, and leaders just like any other broker.

From Kafka documentation:

“The controller is one of the brokers in the cluster and continues to perform all normal broker functions.”

A → Correct: The controller is also a normal broker with partitions.

B/C/D → Incorrect: Replication and consumer fetches are not tied to the controller.

Page Reference:

Kafka: The Definitive Guide, 1st Edition, Chapter 6, p. 190

Apache Kafka Documentation: Kafka Controller Behavior

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Kafka uses ZooKeeper as its distributed coordination system. ZooKeeper helps manage cluster metadata and leader election.

A → Correct: Brokers retrieve leader/partition assignments from ZooKeeper.

D → Correct: ZooKeeper is used to elect the controller broker responsible for cluster-wide administrative tasks.

From Kafka documentation:

“ZooKeeper maintains metadata for Kafka, including broker registration and controller election.”

B → Incorrect: Broker scaling is not handled via ZooKeeper.

C → Incorrect: Replication across clusters is not related to ZooKeeper.

Page Reference:

Kafka: The Definitive Guide, 1st Edition, Chapter 6, p. 189–191

Apache Kafka Documentation: ZooKeeper Integration

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Each Kafka partition is associated with log files and index files that are opened and maintained by the broker. Increasing the number of partitions increases the number of file descriptors (open files). Therefore, it is critical to verify that the broker's system-level max open file count (ulimit -n) is sufficiently high.

From Kafka documentation:

“Each partition uses multiple open files. When the number of partitions increases significantly, make sure to increase the max open file descriptors allowed for the Kafka process.”

C → Correct: Prevents file descriptor exhaustion.

A/B/D → Unrelated to partition count scalability.

Page Reference:

Kafka: The Definitive Guide, 1st Edition, Chapter 6, p. 192

Apache Kafka Documentation: Production Deployment and Scaling Tips

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Kafka supports topic deletion via the kafka-topics.sh tool if the broker property delete.topic.enable=true. This is the recommended and cleanest method to delete a topic and its data from the cluster.

From Kafka documentation:

“To delete a topic, use kafka-topics.sh with --delete flag, provided delete.topic.enable is set to true on the broker.”

A → Correct: Official method.

B → Misunderstood use of null keys (used for compaction).

C → Not recommended; causes inconsistent state.

D → Deletes consumer state, not the topic itself.

Page Reference:

Kafka: The Definitive Guide, 1st Edition, Chapter 6, p. 198

Apache Kafka Documentation: Topic Deletion

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Partitions are Kafka’s core mechanism for scaling and distributing load. Each partition of a topic is distributed across brokers. Producers and consumers interact with specific partitions, and Kafka automatically balances those partitions across available brokers.

From Kafka documentation:

“Partitions allow Kafka to parallelize data and distribute load across brokers and clients.”

A → Correct: Distributes data and load

B → Incorrect: Replicas are for fault tolerance, not load balancing

C → Incorrect: Offsets track read progress, not distribute load

D → Incorrect: Connectors are part of Kafka Connect, not used for client balancing

Page Reference:

Kafka: The Definitive Guide, 1st Edition, Chapter 4, p. 132–134

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When a broker is shut down gracefully:

It flushes data from memory to disk to avoid log recovery upon restart.

It also notifies the controller to reassign leadership of the partitions it leads, ensuring high availability.

From Kafka documentation:

“A graceful shutdown will first flush logs to disk and then hand over leadership of partitions to ensure minimal disruption.”

C → Incorrect: Leader election happens only when the controller detects broker loss; it’s not guaranteed on restart.

D → Incorrect: Partition rebalancing is not automatic on broker restart unless manually initiated.

Page Reference:

Kafka: The Definitive Guide, 1st Edition, Chapter 10, p. 306

Apache Kafka Documentation: Graceful Shutdown Behavior

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Kafka consumers in a consumer group consume partitions exclusively. With 4 partitions and only 2 consumers, each consumer is processing 2 partitions. To increase parallelism, more consumers can be added to the group (up to 4) so that each consumer handles fewer partitions, which reduces the lag.

From Kafka documentation:

“Increasing the number of consumers in a group increases parallelism and helps reduce lag, assuming there are enough partitions.”

A → Ineffective unless followed by rebalancing and consumer scaling.

B → Would create a separate group and not share load.

C → Not applicable; consumers don’t acknowledge messages like producers.

Page Reference:

Kafka: The Definitive Guide, 1st Edition, Chapter 4, p. 127–129

Apache Kafka Documentation: Consumer Group Behavior

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