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Apache Kafka supports several security protocols for configuring broker listeners, which define how clients connect to brokers securely or insecurely. As per the official Apache Kafka documentation (“Security Overview” and “Broker Configuration” sections):

PLAINTEXT:“PLAINTEXT – Unauthenticated, unencrypted connections.”This is the default protocol when no security is configured.

SSL:“SSL – SSL authentication and encryption.”It provides secure communication between clients and brokers using SSL/TLS.

SASL_SSL:“SASL_SSL – SASL authentication and SSL encryption.”This combines SASL-based authentication with SSL encryption for secure and authenticated connections.

SASL_PLAINTEXTis also supported, but it was not listed in your options.

Option C (SASL)is not a valid protocol on its own—it’s a mechanism used within SASL_PLAINTEXT or SASL_SSL.

Option E (GSSAPI)is a SASL mechanism (often used with Kerberos), not a protocol.

Therefore, the correct answers are:

A. PLAINTEXT

B. SSL

D. SASL_SSL

Page Reference:

Confluent Kafka: The Definitive Guide, 1st Edition, Chapter 9 (“Kafka Security”), p. 282–283.

Apache Kafka Documentation, “Security Overview” and “Configuring Broker Listeners”.

To ensure there is no single point of failure in Kafka, the most critical setting is the replication factor. By setting the replication factor to 3, each partition’s data is replicated across three brokers. This provides fault tolerance and ensures data remains available even if one or two brokers fail.

From Kafka documentation:

“A higher replication factor provides better fault tolerance. A replication factor of 3 is a common choice for production systems that prioritize availability and durability.”

A → Incorrect: min.insync.replicas=1 allows data to be acknowledged even if only one replica is in-sync — not resilient to broker failure.

B → Incorrect: More partitions increase parallelism, not redundancy.

C → Incorrect: linger.ms is a producer-side performance setting, unrelated to durability.

D → Correct: Ensures data is redundantly stored across brokers.

Page Reference:

Kafka: The Definitive Guide, 1st Edition, Chapter 6, p. 191–193

Apache Kafka Documentation: Topic-Level Configuration – replication.factor

Let me know if you’d like to continue with Question No: 55!

To increase throughput, Kafka recommends allowing larger batches and giving more time for the producer to accumulate records into batches:

batch.size: Increasing this allows more records to be sent in a single request.

linger.ms: Increasing this adds a delay so that the producer can accumulate more messages into a batch.

From Kafka documentation:

“Increasing both batch.size and linger.ms allows the producer to send larger, more efficient batches, improving throughput at the cost of slightly increased latency.”

Page Reference:

Kafka: The Definitive Guide, 1st Edition, Chapter 5, p. 162–163

Apache Kafka Documentation: Producer Configuration Parameters

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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 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”

Event stream processing is ideal for real-time and near-real-time scenarios. Kafka Streams and ksqlDB are commonly used in use cases requiring immediate reaction to data.

A → Correct: Fraud detection systems benefit from low-latency anomaly detection.

B → Correct: Real-time product recommendations based on user activity require continuous processing.

C → Incorrect: End-of-day processing is batch-oriented, not real-time.

D → Correct: Real-time alerting and log anomaly detection are classic streaming use cases.

E → Incorrect: Historical dashboards rely on batch aggregation and are not continuous processing.

Page Reference:

Kafka: The Definitive Guide, 1st Edition, Chapter 7, p. 210–215

Confluent Documentation: Use Cases for Kafka Streams and ksqlDB

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The kafka-consumer-groups.sh script is used to monitor consumer group offsets and lag. It shows the current offset, log end offset, and lag for each partition consumed by a consumer group.

From Kafka documentation:

“You can use kafka-consumer-groups.sh with the --describe flag to get the offset lag per consumer group.”

A → Incorrect: No such built-in script.

B → Correct: Official script for viewing consumer group lag.

C → Incorrect: Does not exist as a built-in Kafka tool.

D → Incorrect: Used for partition reassignment, unrelated to lag monitoring.

Page Reference:

Kafka: The Definitive Guide, 1st Edition, Chapter 10, p. 308

Apache Kafka Documentation: kafka-consumer-groups.sh usage

Let’s break it down step by step using official Kafka architecture principles:

Total partitions: 10

Each partition stores: 25 GB per day

Total data for the topic per day (no replication): 10 partitions × 25 GB = 250 GB

Replication factor: 3 → three copies of each partition across the brokers

Each partition is replicated across three brokers. Since the replication factor is three, each broker will have one full replica of one-third of the partitions. In a balanced cluster with three brokers:

Each broker stores a replica of every partition (since replication factor = number of brokers).

So, each broker will store the full 250 GB of data for that topic.

Hence:

Each broker will consume: 250 GB

This is confirmed in the official Apache Kafka documentation:

“A replica is a fully-qualified copy of a partition. When the replication factor equals the number of brokers, each broker contains a full copy of the topic data.”

“Each broker stores the total size of data for the topic, regardless of which partitions it leads.”

Page Reference:

Confluent Kafka: The Definitive Guide, 1st Edition, Chapter 6 (Kafka Cluster), p. 209–210.

Apache Kafka Documentation, “Replication” and “Storage” sections.

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To ensure that duplicate messages are not processed and no messages are skipped, ksqlDB’s persistent queries must be configured to provide exactly-once processing guarantees. This is achieved by setting the processing.guarantee property to exactly_once. This configuration ensures that messages are neither lost nor processed more than once in the event of failures or restarts.

As per the official Confluent documentation:

“ksqlDB persistent queries can be configured for either at-least-once or exactly-once processing semantics by setting the processing.guarantee configuration property.”

“When processing.guarantee is set to exactly_once, ksqlDB ensures that messages are processed only once, even in failure scenarios. This eliminates duplicates and guarantees strong message delivery semantics.”

Other options:

Option B and C (ksql.streams.auto.offset.reset) only control the starting offset for new consumers, and do not enforce exactly-once semantics.

Option D (ksql.fail.on.production.error) controls whether errors during message production should fail the query, and does not affect exactly-once processing.

Page Reference:

Confluent Kafka: The Definitive Guide, 1st Edition, Chapter 11 (Stream Processing with ksqlDB), p. 365.

Confluent Documentation, “ksqlDB Configuration Properties” (processing.guarantee).

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Kafka Connect allows parallelism via tasks. The setting tasks.max=1 limits the connector to a single task, meaning it can only process one table at a time. To increase throughput and reduce lag, the number of tasks should be increased, allowing parallel processing of multiple tables.

From Kafka documentation:

“A single connector can have multiple tasks to maximize parallelism. Increasing tasks.max allows the connector to process more data in parallel.”

A → Increasing Connect nodes won’t help unless more tasks are configured.

B → Correct: Enables parallelism for each table.

C → Unrelated to source connector lag.

D → Increasing replication factor doesn’t improve connector performance.

Page Reference:

Kafka: The Definitive Guide, 1st Edition, Chapter 8, p. 245–246

Apache Kafka Documentation: Kafka Connect Concepts – tasks.max

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