Apache Kafka Practice Exam 2025 – The Complete All-in-One Guide for Exam Success!

The 'consumer lag' metric in Kafka indicates the difference between produced and consumed message offsets. This metric is crucial for understanding the performance and efficiency of a consumer group. Specifically, it tells you how many messages have been produced to a topic that have not yet been processed (or consumed) by the consumers in the group.

When consumers fall behind in processing messages, the consumer lag will increase, reflecting the volume of unprocessed messages. Monitoring this lag is essential for ensuring that the consumers are keeping pace with the incoming message flow and for diagnosing potential issues such as slow consumers or misconfigured systems. By understanding consumer lag, Kafka administrators can take appropriate steps to optimize the performance of their consumer applications, such as scaling out by adding more consumers or tuning existing ones for better performance.

Other options do not accurately capture the essence of consumer lag. For instance, the number of active consumers in a group is a separate metric that indicates the scale of consumer instances without giving insights into message processing performance. Similarly, the time taken for consumers to process messages relates more to the processing speed than their ability to keep up with the message stream. Lastly, the speed at which messages are replicated pertains to data redundancy and fault tolerance, which is not connected to the consumption of messages