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Mapr (HP) Hadoop Developer Certification Questions and Answers (Dumps and Practice Questions)



Question : Using Hadoop mapreduce framework, you have to use org.apache.hadoop.mapred.lib.IdentityMapper java class as a Mapper and /bin/wc as a reducer.
Select the correct option from below command.

: Using Hadoop mapreduce framework, you have to use org.apache.hadoop.mapred.lib.IdentityMapper java class as a Mapper and /bin/wc as a reducer.
1. $HADOOP_HOME/bin/hadoop jar $HADOOP_HOME/hadoop-streaming.jar \
-input myInputDirs \
-output myOutputDir \
-mapR org.apache.hadoop.mapred.lib.IdentityMapper -reducer /bin/wc


2. $HADOOP_HOME/bin/hadoop \
-input myInputDirs \
-output myOutputDir \
-mapper org.apache.hadoop.mapred.lib.IdentityMapper \
-reducer /bin/wc


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-input myInputDirs \
-output myOutputDir \
-map org.apache.hadoop.mapred.lib.IdentityMapper \
-red /bin/wc


4. $HADOOP_HOME/bin/hadoop jar $HADOOP_HOME/hadoop-streaming.jar \
-input myInputDirs \
-output myOutputDir \
-mapper org.apache.hadoop.mapred.lib.IdentityMapper \
-reducer /bin/wc

Correct Answer : Get Lastest Questions and Answer :
Explanation: You can supply a Java class as the mapper and/or the reducer. The above example is equivalent to:

$HADOOP_HOME/bin/hadoop jar $HADOOP_HOME/hadoop-streaming.jar \
-input myInputDirs \
-output myOutputDir \
-mapper org.apache.hadoop.mapred.lib.IdentityMapper \
-reducer /bin/wc





Question : By default, streaming tasks exiting with non-zero status are considered to be _________ tasks.

: By default, streaming tasks exiting with non-zero status are considered to be _________ tasks.
1. Failure

2. Success

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Correct Answer : Get Lastest Questions and Answer :
Explanation: User can specify stream.non.zero.exit.is.failure as true or false to make a streaming task that exits with a non-zero status to be Failure or Success
respectively. By default, streaming tasks exiting with non-zero status are considered to be failed tasks.





Question : You have written your Python code as a Mapper for MapReduce job in a file called "myPythonScript.py". To run MapReduce job, You have to transfer this Python file on
each node of the cluster, before starting job.
: You have written your Python code as a Mapper for MapReduce job in a file called
1. True
2. False

Correct Answer : Get Lastest Questions and Answer :
Explanation: You can specify any executable as the mapper and/or the reducer. The executables do not need to pre-exist on the machines in the cluster


Related Questions

Question : All keys used for intermediate output from mappers must:
 : All keys used for intermediate output from mappers must:
1. Implement a splittable compression algorithm.
2. Be a subclass of FileInputFormat.
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4. Override isSplitable.
5. Implement a comparator for speedy sorting.


Question : Which Hadoop component is responsible for managing the distributed file system metadata?

 : Which Hadoop component is responsible for managing the distributed file system metadata?
1. NameNode
2. Metanode
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4. NameSpaceManager


Question : You need to move a file titled "weblogs" into HDFS. When you try to copy the file, you can't.
You know you have ample space on your DataNodes. Which action should you take to
relieve this situation and store more files in HDFS?

 : You need to move a file titled
1. Increase the block size on all current files in HDFS.
2. Increase the block size on your remaining files.
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4. Increase the amount of memory for the NameNode.
5. Increase the number of disks (or size) for the NameNode.


Question : In the reducer, the MapReduce API provides you with an iterator over Writable values.
What does calling the next () method return?


 : In the reducer, the MapReduce API provides you with an iterator over Writable values.
1. It returns a reference to a different Writable object time.
2. It returns a reference to a Writable object from an object pool.
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4. It returns a reference to a Writable object. The API leaves unspecified whether this is a reused object or a new object.
5. It returns a reference to the same Writable object if the next value is the same as the previous value, or a new Writable object otherwise.


Question : MapReduce v (MRv/YARN) splits which major functions of the JobTracker into separate daemons? Select two.
A. Heath states checks (heartbeats)
B. Resource management
C. Job scheduling/monitoring
D. Job coordination between the ResourceManager and NodeManager
E. Launching tasks
F. Managing file system metadata
G. MapReduce metric reporting
H. Managing tasks
 : MapReduce v (MRv/YARN) splits which major functions of the JobTracker into separate daemons? Select two.
1. B,C
2. A,D
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4. C,H
5. B,G


Question : For each input key-value pair, mappers can emit:
 : For each input key-value pair, mappers can emit:
1. As many intermediate key-value pairs as designed. There are no restrictions on the
types of those key-value pairs (i.e., they can be heterogeneous).
2. As many intermediate key-value pairs as designed, but they cannot be of the same type
as the input key-value pair.
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4. One intermediate key-value pair, but of the same type.
5. As many intermediate key-value pairs as designed, as long as all the keys have the
same types and all the values have the same type.