Cloudera Certified Administrator for Apache Hadoop (CCAH) : CCA-500

During the execution of a MapReduce v2 (MRv2) job on YARN, where does the Mapper place the intermediate data of
each Map Task?

• A. YARN holds the intermediate data in the NodeManager's memory (a container) until it is transferred to the Reducer
• B. The Mapper stores the intermediate data in HDFS on the node where the Map tasks ran in the HDFS
  /usercache/&(user)/apache/application_&(appid) directory for the user who ran the job
• C. The Mapper transfers the intermediate data immediately to the reducers as it is generated by the Map Task
• D. The Mapper stores the intermediate data on the underlying filesystem of the local disk in the directories
  yarn.nodemanager.locak-DIFS
• E. The Mapper stores the intermediate data on the node running the Job's ApplicationMaster so that it is available to
  YARN ShuffleService before the data is presented to the Reducer

You're upgrading a Hadoop cluster from HDFS and MapReduce version 1 (MRv1) to one running HDFS and MapReduce
version 2 (MRv2) on YARN. You want to set and enforce version 1 (MRv1) to one running HDFS and MapReduce
version 2 (MRv2) on YARN. You want to set and enforce a block size of 128MB for all new files written to the cluster
after upgrade. What should you do?

• A. Set dfs.block.size to 128 M on all the worker nodes and client machines, and set the parameter to final. You do not
  need to set this value on the NameNode
• B. Set dfs.block.size to 134217728 on all the worker nodes, on all client machines, and on the NameNode, and set the
  parameter to final
• C. Set dfs.block.size to 134217728 on all the worker nodes and client machines, and set the parameter to final. You do
  not need to set this value on the NameNode
• D. You cannot enforce this, since client code can always override this value
• E. Set dfs.block.size to 128M on all the worker nodes, on all client machines, and on the NameNode, and set the
  parameter to final

Which scheduler would you deploy to ensure that your cluster allows short jobs to finish within a reasonable time
without starting long-running jobs?

• A. Capacity Scheduler
• B. Complexity Fair Scheduler (CFS)
• C. FIFO Scheduler
• D. Fair Scheduler

Assume you have a file named foo.txt in your local directory. You issue the following three commands:
Hadoop fs -mkdir input
Hadoop fs -put foo.txt input/foo.txt
Hadoop fs -put foo.txt input
What happens when you issue the third command?

• A. You get a error message telling you that foo.txt already exists. The file is not written to HDFS
• B. The write silently fails
• C. You get a warning that foo.txt is being overwritten
• D. The file is uploaded and stored as a plain file named input
• E. You get an error message telling you that input is not a directory
• F. You get an error message telling you that foo.txt already exists, and asking you if you would like to overwrite it.
• G. The write succeeds, overwriting foo.txt in HDFS with no warning

A user comes to you, complaining that when she attempts to submit a Hadoop job, it fails. There is a Directory in HDFS
named /data/input. The Jar is named j.jar, and the driver class is named DriverClass.
She runs the command:
Hadoop jar j.jar DriverClass /data/input/data/output
The error message returned includes the line:
PriviligedActionException as:training (auth:SIMPLE)
cause:org.apache.hadoop.mapreduce.lib.input.invalidInputException:
Input path does not exist: file:/data/input
What is the cause of the error?

• A. The user is not authorized to run the job on the cluster
• B. The Hadoop configuration files on the client do not point to the cluster
• C. The name of the driver has been spelled incorrectly on the command line
• D. The directory name is misspelled in HDFS
• E. The output directory already exists