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What is a common cause of a Spark job failing with out of memory errors?

  1. A A wide transformation or skewed key causing a single executor to hold too much data
  2. B Too many small files
  3. C Insufficient disk space
  4. D Incorrect file format
Answer

A wide transformation or skewed key causing a single executor to hold too much data

Salting skewed keys, increasing partitions, or replacing a collect with a distributed write are the usual remedies.

All Big data MCQs

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