When a MapReduce task fails, the task can be recalculated through the retry mechanism.

Question

Options

• ATrue
• BFalse

• A
  74% (23)
• B
  26% (8)

Explanation

Option A is correct because MapReduce is designed with fault tolerance as a core feature - when a task (either a Map or Reduce task) fails due to worker crashes, network issues, or other errors, the master node detects the failure and reschedules the task on another available worker node for re-execution. This retry mechanism is automatic and transparent to the application developer, making MapReduce robust for large-scale distributed processing. Option B is wrong because it contradicts the fundamental design of MapReduce; without retry capability, a single worker failure would cause the entire job to fail, which would make MapReduce impractical for real-world use at scale.

Memory tip: Think of MapReduce's retry mechanism like a relay race where if a runner drops the baton, another runner picks it up and continues - the race (job) never has to start over from scratch just because one participant failed.

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