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



Question : Which of the following in-built counter will help us to find total number of Bytes written on local file system. During job execution

: Which of the following in-built counter will help us to find total number of Bytes written on local file system. During job execution
1. FILE_BYTES_WRITTEN

2. MAPRFS_BYTES_WRITTEN

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4. LOCAL_BYTES_WRITTEN

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Explanation: 1. Filesystem counters
Filesystem counters are used to analysis experimental results. The following are the typical built-in filesystem counters.
Local file system
FILE_BYTES_READ
FILE_BYTES_WRITTEN
HDFS file system
HDFS_BYTES_READ
HDFS_BYTES_WRITTEN
FILE_BYTES_READ is the number of bytes read by local file system. Assume all the map input data comes from HDFS, then in map phase FILE_BYTES_READ should be
zero. On the other hand, the input file of reducers are data
on the reduce-side local disks which are fetched from map-side disks. Therefore, FILE_BYTES_READ denotes the total bytes read by reducers.

FILE_BYTES_WRITTEN consists of two parts. The first part comes from mappers. All the mappers will spill intermediate output to disk. All the bytes that
mappers write to disk will be included in FILE_BYTES_WRITTEN.
The second part comes
from reducers. In the shuffle phase, all the reducers will fetch intermediate data from mappers and merge and spill to reducer-side disks. All the bytes
that reducers write to disk will also be included in FILE_BYTES_WRITTEN.

HDFS_BYTES_READ denotes the bytes read by mappers from HDFS when the job starts. This data includes not only the content of source file but also metadata
about splits.

HDFS_BYTES_WRITTEN denotes the bytes written to HDFS. It s the number of bytes of the final output.

Note that since HDFS and local file systems are different file systems so the data from the two file systems will never overlap.




Question : label-based scheduling, help us to override the default scheduling algorithm and run tasks on specific nodes
: label-based scheduling, help us to override the default scheduling algorithm and run tasks on specific nodes
1. True
2. False

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Explanation:




Question : While doing MRUnit test, you provide input key and value as well as expected output. What happens if the actual output does not match the
expected output
: While doing MRUnit test, you provide input key and value as well as expected output. What happens if the actual output does not match the
1. Test case will fail and driver will throw an exception

2. Test case will fail and no exception from driver

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4. Any of the above can happen


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Explanation:


Related Questions

Question : Please map the followings
A. Find all the Running Jobs
B. Get the completion status of a Particular job
C. Stop already running Job

1. hadoop job -list
2. hadoop job -kill job_id
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: Please map the followings
1. A-1, B-2, C-3
2. A-1, B-3, C-2
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4. A-3, B-1, C-2
5. A-3, B-2, C-3


Question : Which is/are the correct ways to set the Job Priority from below?


 : Which is/are the correct ways to set the Job Priority from below?
1. Configuration conf = new Configuration
conf.set("mapred.job.priority" , "VERY_LOW")


2. Passing as a parameter while submitting job
-D mapred.job.priority=VERY_LOW


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hadoop job -set-priority job_id


4. 1,2

5. 1,2,3



Question : You can use the Job Priority to prioritize your Job Over other Jobs in other Pools or Queue
: You can use the Job Priority to prioritize your Job Over other Jobs in other Pools or Queue
1. True
2. False


Question : In the label-based scheduling


: In the label-based scheduling
1. User can override the default scheduling algorithm and can have more control where the Job should run the Cluster

2. Location of the labels file can be defined using jobtracker.node.labels.file in mapred-site.xml file

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4. 1,2

5. 1,2,3



Question : Select correct statement regarding label based scheduling


: Select correct statement regarding label based scheduling
1. To list all the available labels in the cluster, you can use hadoop job -showlabels

2. We can use following command line option to Submit job with label hadoop jar -D mapred.job.label=hadoopexam

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4. 1,2

5. 1,2,3



Question : You have following command executed

hadoop job -showlables
Node lables :
CentOS001 : [heavy, hig_ram, high_cpu]
CentOS002 : [light, low_ram, low_cpu]
CentOS003 : [medium, m_ram, m_cpu]

Ans now you submit the job with below command

hadoop jar -D mapred.job.label=hadoopexam

What would happen?
 : You have following command executed
1. It will submit the entire job on CentOS001

2. It will submit the entire job on CentOS002

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4. It will use default scheduling algorithm

5. Job will hang