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Welcome to the MapReduce Development and Features MCQs Page

Dive deep into the fascinating world of MapReduce Development and Features with our comprehensive set of Multiple-Choice Questions (MCQs). This page is dedicated to exploring the fundamental concepts and intricacies of MapReduce Development and Features, a crucial aspect of Hadoop. In this section, you will encounter a diverse range of MCQs that cover various aspects of MapReduce Development and Features, from the basic principles to advanced topics. Each question is thoughtfully crafted to challenge your knowledge and deepen your understanding of this critical subcategory within Hadoop.

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Check out the MCQs below to embark on an enriching journey through MapReduce Development and Features. Test your knowledge, expand your horizons, and solidify your grasp on this vital area of Hadoop.

Note: Each MCQ comes with multiple answer choices. Select the most appropriate option and test your understanding of MapReduce Development and Features. You can click on an option to test your knowledge before viewing the solution for a MCQ. Happy learning!

MapReduce Development and Features MCQs | Page 2 of 11

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Q11.
Users can control which keys (and hence records) go to which Reducer by implementing a custom?

a.

Partitioner

b.

OutputSplit

c.

Reporter

d.

All of the mentioned

Discuss
Answer: (a).Partitioner
Q12.
Applications can use the ____________ to report progress and set application-level status messages.

a.

Partitioner

b.

OutputSplit

c.

Reporter

d.

All of the mentioned

Discuss
Answer: (c).Reporter
Q13.
The right level of parallelism for maps seems to be around _________ maps per-node.

a.

1-10

b.

10-100

c.

100-150

d.

150-200

Discuss
Answer: (b).10-100
Q14.
The number of reduces for the job is set by the user via _________

a.

JobConf.setNumTasks(int)

b.

JobConf.setNumReduceTasks(int)

c.

JobConf.setNumMapTasks(int)

d.

All of the mentioned

Discuss
Answer: (b).JobConf.setNumReduceTasks(int)
Q15.
The framework groups Reducer inputs by key in _________ stage.

a.

sort

b.

shuffle

c.

reduce

d.

none of the mentioned

Discuss
Answer: (a).sort
Q16.
The output of the reduce task is typically written to the FileSystem via _____________

a.

OutputCollector.collect

b.

OutputCollector.get

c.

OutputCollector.receive

d.

OutputCollector.put

Discuss
Answer: (a).OutputCollector.collect
Q17.
Which of the following is the default Partitioner for Mapreduce?

a.

MergePartitioner

b.

HashedPartitioner

c.

HashPartitioner

d.

None of the mentioned

Discuss
Answer: (c).HashPartitioner
Q18.
Which of the following partitions the key space?

a.

Partitioner

b.

Compactor

c.

Collector

d.

All of the mentioned

Discuss
Answer: (a).Partitioner
Q19.
____________ is a generalization of the facility provided by the MapReduce framework to collect data output by the Mapper or the Reducer.

a.

OutputCompactor

b.

OutputCollector

c.

InputCollector

d.

All of the mentioned

Discuss
Answer: (b).OutputCollector
Q20.
__________ is the primary interface for a user to describe a MapReduce job to the Hadoop framework for execution.

a.

JobConfig

b.

JobConf

c.

JobConfiguration

d.

All of the mentioned

Discuss
Answer: (b).JobConf
Page 2 of 11

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