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

Question : Using the Combiner will increase the network overhead ?


1. True
2. False

Correct Answer : Get Lastest Questions and Answer :


Explanation: Often, Mappers produce large amounts of intermediate data
- The data must be passed to the Reducers
- This can result in a lot of network traffic.

You can specify the Combiner, which is consider mini-reducer
- Combiner runs locally on a single Mappers output.
- Output from the Combiner is sent to the Reducers.
- Input and Output data types for the Combiner and Reducer must be identical.

Combiner can be applied only when operation performed is commutative and associative.

Refer HadoopExam.com Recorded Training Module : 3Often, Mappers produce large amounts of intermediate data
- The data must be passed to the Reducers
- This can result in a lot of network traffic.

You can specify the Combiner, which is consider mini-reducer
- Combiner runs locally on a single Mappers output.
- Output from the Combiner is sent to the Reducers.
- Input and Output data types for the Combiner and Reducer must be identical.

Combiner can be applied only when operation performed is commutative and associative.

Note : The Combiner may run once, or more than once, on the output from any given Mapper.

Do not put the in the Combiner which could influence your results if it runs more than once.


Refer HadoopExam.com Recorded Training Module : 3





Question : A combiner reduce the amount of data sent to the Reducer ?


1. True
2. False

Correct Answer : Get Lastest Questions and Answer :


Explanation: Often, Mappers produce large amounts of intermediate data
- The data must be passed to the Reducers
- This can result in a lot of network traffic.

You can specify the Combiner, which is consider mini-reducer
- Combiner runs locally on a single Mappers output.
- Output from the Combiner is sent to the Reducers.
- Input and Output data types for the Combiner and Reducer must be identical.

Combiner can be applied only when operation performed is commutative and associative.

Note : The Combiner may run once, or more than once, on the output from any given Mapper.

Do not put the in the Combiner which could influence your results if it runs more than once.


Refer HadoopExam.com Recorded Training Module : 3






Question : Combiner reduces the network traffic but increases the amount of work needed to be done by the reducer ?


1. True
2. False

Correct Answer : Get Lastest Questions and Answer :


Explanation: Combiner decreases the amount of network traffic required during the shuffle and sort phase
and often also decreases the amount of work needed to be done by the reducer.

Often, Mappers produce large amounts of intermediate data
- The data must be passed to the Reducers
- This can result in a lot of network traffic.

You can specify the Combiner, which is consider mini-reducer
- Combiner runs locally on a single Mappers output.
- Output from the Combiner is sent to the Reducers.
- Input and Output data types for the Combiner and Reducer must be identical.

Combiner can be applied only when operation performed is commutative and associative.

Note : The Combiner may run once, or more than once, on the output from any given Mapper.

Do not put in the Combiner which could influence your results if it runs more than once.


Refer HadoopExam.com Recorded Training Module : 3

Related Questions

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Question : To analyze the website click of HadoopExam.com you have written a Mapreduce job, which
will product the click reports for each week e.g. 53 reports for whole year.Which of the following Hadoop API class you must use
so that output file generated as per the weeks and output data will go in corresponding output file.

1. Hive
2. MapReduce Chaining
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4. Partitioner


Question : Reducers are generally helpful to write the job ouput data in desried location or database.
In your ETL MapReduce job you set the number of reducer to zero, select the correct statement which applies.

1. You can not configure number of reducer
2. No reduce tasks execute. The output of each map task is written to a separate file in HDFS
3. Access Mostly Uused Products by 50000+ Subscribers
4. You can not configure number of reducer, it is decided by Tasktracker at runtime


Question : In the QuickTechie website log file named as MAIN.PROFILES.log you have keys are (ipaddres+locations), and the values are Number of clicks (int).
For each unique key (string), you want to find the average of all values associated with each key. In writing a MapReduce program to accomplish this, can you take advantage of a
combiner?

1. No, best way to accomplish this you have to use Aapche Pig
2. No, best way to accomplish this you have to use MapReduce chaining.
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4. Yes


Question : In our website www.HadoopExam.com we have Million profiles and created ETL jobs for processing this file.
You have submitted a ETL mapReduce job for HadoopExam.com websites log file analysis as well as combining profile data to Hadoop
and notice in the JobTracker's Web UI that the Mappers are 80% complete
while the reducers are 20% complete. What is the best explanation for this?

1. The progress attributed to the reducer refers to the transfer of data from completed Mappers.
2. The progress attributed to the reducer refers to the transfer of data from Mappers is still going on.
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4. The progress attributed to the reducer refers to the transfer of data from Mappers an not be predicted.


Question : In your MapReduce job, you have three configuration parameters.
What is the correct or best way to pass a these three configuration parameters to a mapper or reducer?

1. As key pairs in the Configuration object.
2. As value pairs in the Configuration object.
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4. Not possible


Question : In word count MapReduce algorithm, why might using a combiner (Combiner, runs after the Mapper and before the Reducer. )
reduce the overall job running time?

1. combiners perform local filtering of repeated word, thereby reducing the number of key-value pairs that need to be shuffled across the network to the reducers.
2. combiners perform global aggregation of word counts, thereby reducing the number of key-value pairs that need to be shuffled across the network to the reducers.
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4. combiners perform local aggregation of word counts, thereby reducing the number of key-value pairs that need to be shuffled across the network to the reducers.