MapReduce Framework and Its Implementation in Hadoop DATA-SCIENCE-OPTIMIZE Practice Questions (Page 4)

Questions 16–20

1. 16application · medium

   A MapReduce job that joins two large datasets is running slowly. The job uses a reduce-side join, and the mappers emit all records from both datasets. The administrator notices that the shuffle phase is transferring a large amount of data. Which optimization will most directly reduce the amount of data shuffled?

   • AUse a map-side join by loading the smaller dataset into the distributed cache.
   • BIncrease the number of reducers.
   • CSet mapreduce.job.reduce.slowstart.completedmaps to 0.5
   • DEnable compression for the map output.
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2. 17application · medium

   A Hadoop administrator is troubleshooting a MapReduce job that fails with an 'OutOfMemory' error in the reduce phase. The job processes large values, and the reduce function needs to hold a significant amount of data in memory. Which configuration change is most likely to resolve the issue?

   • ASet mapreduce.reduce.speculative to true
   • BIncrease mapreduce.reduce.memory.mb
   • CIncrease the number of reducers
   • DDecrease mapreduce.reduce.input.buffer.percent
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4. 18foundation · easy

   Which sequence correctly represents the execution flow of a Hadoop MapReduce job?

   • AMap → Input split → Reduce → Shuffle and sort → Output
   • BInput split → Reduce → Map → Shuffle and sort → Output
   • CShuffle and sort → Input split → Map → Reduce → Output
   • DInput split → Map → Shuffle and sort → Reduce → Output
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5. 19expert · hard

   A MapReduce job is running with a large number of reducers, but the reduce phase is underutilized because the map output is skewed. Some reducers receive much more data than others. The administrator wants to improve the job's performance by balancing the reduce load. Which approach is most effective?

   • AIncrease the number of reducers to reduce the data per reducer.
   • BUse a custom partitioner that distributes keys based on the actual data distribution.
   • CSet mapreduce.reduce.input.buffer.percent to a higher value.
   • DEnable speculative execution for reduce tasks.
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6. 20foundation · easy

   What is the main benefit of using a combiner in a Hadoop MapReduce job?

   • AIt ensures that all keys are sorted before the reduce phase.
   • BIt provides fault tolerance by replicating intermediate data.
   • CIt increases the number of reducers to improve parallelism.
   • DIt reduces the amount of data transferred from mappers to reducers.
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