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

Setting the number of retries to a maximum limit is an important strategy when dealing with critical data scenarios. This configuration allows you to effectively manage and control the flow of messages and the response mechanisms in cases of failure. By establishing a maximum limit on retries, you can ensure that the system won't endlessly attempt to resend messages, which can lead to increased load, resource exhaustion, or potential data duplication.

In critical data scenarios, it's crucial to balance between resilience and resource management. With a defined maximum number of retries, you provide a structured approach that enables the system to recover from transient issues while avoiding unnecessary long-term loops of failure. If the message still fails after reaching the maximum retry limit, you can implement a fallback strategy, such as sending notifications or logging the errors, which allows the team to address the issue manually or through other automated processes.

Other options suggest approaches that could introduce long-term issues or inefficiencies. For instance, retrying until manual intervention occurs may lead to indefinite waiting times and system bottlenecks, while disabling retries altogether would risk losing critical data. On the other hand, retrying every second could overwhelm the system and lead to performance degradation, especially if many messages are being processed simultaneously. Thus, setting a maximum retry limit strikes the