MapReduce Framework and Its Implementation in Hadoop DATA-SCIENCE-OPTIMIZE Practice Questions (Page 3)

Questions 11–15

1. 11foundation · easy

   Which statement correctly describes the MapReduce programming model?

   • AIt processes data in a single pass, with each record handled independently by a single process.
   • BIt splits input into independent chunks, processes them in parallel during a map phase, and then aggregates intermediate results during a reduce phase.
   • CIt is a programming model that only works with structured relational data.
   • DIt requires all data to be loaded into memory before processing begins.
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2. 12foundation · easy

   Which configuration parameter in Hadoop MapReduce controls the number of reduce tasks for a job?

   • Amapreduce.job.maps
   • Bmapreduce.task.io.sort.mb
   • Cmapreduce.map.memory.mb
   • Dmapreduce.job.reduces
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4. 13foundation · easy

   What is the primary function of a partitioner in Hadoop MapReduce?

   • ATo combine multiple values with the same key on the map side.
   • BTo sort the intermediate keys before they are sent to the reducers.
   • CTo determine which reducer receives each intermediate key-value pair.
   • DTo compress the intermediate data to reduce network traffic.
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5. 14foundation · easy

   Which input format in Hadoop MapReduce is commonly used to process plain text files where each line is a record?

   • ADBInputFormat
   • BKeyValueTextInputFormat
   • CTextInputFormat
   • DSequenceFileInputFormat
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6. 15foundation · easy

   What is the purpose of speculative execution in Hadoop MapReduce?

   • ATo test different algorithms on the same data and select the best result.
   • BTo run multiple copies of the same task on different nodes and use the first one that finishes.
   • CTo backup the output of each task to HDFS for fault tolerance.
   • DTo pre-compute results for future queries.
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