What is the purpose of fault tolerance mechanisms in MapReduce frameworks?

• A. To allow the system to continue by re-executing failed tasks.
• B. To prevent data replication.
• C. To ensure a single reducer handles all data.
• D. To eliminate the need for mappers.

Answer: (A)

The main idea being tested is how MapReduce stays reliable in the face of failures. In a distributed job, individual tasks can crash or slow down; fault tolerance mechanisms ensure the overall job keeps moving by re-executing those failed tasks on healthy workers. When a map or reduce task fails, the framework automatically reschedules it, reprocesses its input, and continues toward completion without user intervention. This re-execution capability is what lets large data processing jobs finish reliably even when parts of the cluster hiccup.

Think about it this way: the system treats work as something that can be redone. It tracks task attempts and, if needed, restarts them on different nodes, often reading the same input again from a replicated storage layer. That way, a single failure doesn’t derail the entire job.

Other options miss the point because they either imply reducing protection to data replication as the primary goal, which isn’t about redoing work to survive failures, or they suggest architectural choices like a single reducer or removing mappers, which would undermine parallelism and the whole MapReduce model.