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

Using a replication factor in Apache Kafka primarily addresses fault tolerance and data loss issues. When a topic has a replication factor greater than one, Kafka will create multiple copies of each partition across different brokers. This ensures that if a broker fails or undergoes maintenance, there are still available replicas of the partitions that can serve client requests and prevent data loss.

In the context of fault tolerance, if a partition's leader becomes unavailable, Kafka can promote one of the replicas to become the new leader, allowing the system to continue to operate without interruption. This redundancy is crucial in distributed systems to ensure high availability and durability of data, as it protects against the risk of data loss due to hardware failures, network issues, or any other unforeseen circumstances impacting a broker.

While other choices may seem relevant, they do not specifically align with the functionality of the replication factor in Kafka. For example, data duplication issues are more concerned with how data is produced and consumed rather than with the replication process itself. Network latency problems and performance bottlenecks relate to the speed and efficiency of data transmission and processing, which are not directly mitigated by increasing the replication factor. Rather, these areas are handled through different optimizations and configurations within Kafka's architecture.