Free CCD-410 – Cloudera Certified Developer for Apache Hadoop (CCDH) Practice Test Question

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  Heath states checks (heartbeats)

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  Resource management

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  Job scheduling/monitoring

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  Job coordination between the ResourceManager and NodeManager

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  Launching tasks

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  Managing file system metadata

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  MapReduce metric reporting

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  Managing tasks

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  Single point of failure in the NameNode.

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  Resource pressure on the JobTracker.

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  HDFS latency.

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  Ability to run frameworks other than MapReduce, such as MPI.

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  Reduce complexity of the MapReduce APIs.

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  Standardize on a single MapReduce API.
  

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  You will not be able to compress the intermediate data.

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  You will longer be able to take advantage of a Combiner.

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  By using multiple reducers with the default HashPartitioner, output files may not be in globally sorted order.

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  There are no concerns with this approach. It is always advisable to use multiple reduces.
  

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  Increase the parameter that controls minimum split size in the job configuration.

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  Write a custom MapRunner that iterates over all key-value pairs in the entire file.

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  Set the number of mappers equal to the number of input files you want to process.

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  Write a custom FileInputFormat and override the method isSplitable to always return false.
  

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  Six

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  Five

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  Four

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  Two

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  One

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  Three
  

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  SequenceFiles

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  Avro

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  JSON

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  HTML

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  XML

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  CSV
  

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  The file will be marked as corrupted if data node B fails during the creation of the file.

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  Each data node locks the local file to prohibit concurrent readers and writers of the file.

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  Each data node stores a copy of the file in the local file system with the same name as the HDFS file.

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  The file can be accessed if at least one of the data nodes storing the file is available.