Apache Kafka Complete Practice Exam 2025 – All-in-One Prep

The term 'back pressure' in the context of Kafka specifically refers to the situation where consumers cannot keep pace with the rate at which producers are sending messages. This condition can arise when the consumer's processing speed is slower than the producer's message production rate, leading to an accumulation of unprocessed messages in the system.

When back pressure occurs, it can result in several system behaviors, such as increased latency in message consumption, potential memory pressure as the buffer fills up, or even the risk of message loss if the system is not configured to handle such scenarios. Effective handling of back pressure is crucial in distributed data systems to maintain performance and ensure that consumers can eventually catch up with producers.

In contrast, other mentioned scenarios do not directly relate to the consumer's ability to process messages in real-time. For instance, brokers being overloaded with data refers more to the overall capacity limits of the Kafka infrastructure instead of a specific consumer-producer dynamic. Data replication failures concern the durability and availability of messages rather than the flow of data between producers and consumers. Lastly, network latency affecting message delivery pertains to transmission delays rather than the internal dynamics of message processing and flow control.