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Welcome to the Big Data Processing with MapReduce MCQs Page

Dive deep into the fascinating world of Big Data Processing with MapReduce with our comprehensive set of Multiple-Choice Questions (MCQs). This page is dedicated to exploring the fundamental concepts and intricacies of Big Data Processing with MapReduce, a crucial aspect of Big Data Computing. In this section, you will encounter a diverse range of MCQs that cover various aspects of Big Data Processing with MapReduce, 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 Big Data Computing.

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Check out the MCQs below to embark on an enriching journey through Big Data Processing with MapReduce. Test your knowledge, expand your horizons, and solidify your grasp on this vital area of Big Data Computing.

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

Big Data Processing with MapReduce MCQs | Page 7 of 8

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Q61.
What is the purpose of the replication factor in HDFS?

a.

To determine the file's block size

b.

To coordinate read and write requests

c.

To enforce the number of copies of each block in the cluster

d.

To manage metadata for files and directories

Discuss
Answer: (c).To enforce the number of copies of each block in the cluster Explanation:The replication factor in HDFS is used to enforce the number of copies of each block in the cluster.
Q62.
How does HDFS ensure fault tolerance for data blocks?

a.

By replicating data blocks on multiple datanodes

b.

By using a highly available namenode

c.

By increasing the block size for data storage

d.

By performing regular consistency checks on datanodes

Discuss
Answer: (a).By replicating data blocks on multiple datanodes Explanation:HDFS ensures fault tolerance for data blocks by replicating data blocks on multiple datanodes.
Q63.
What is the role of splits in the MapReduce process?

a.

Dividing the reduce tasks

b.

Dividing the input data for parallel processing

c.

Managing metadata for files and directories

d.

Coordinating read and write requests

Discuss
Answer: (b).Dividing the input data for parallel processing Explanation:Splits in the MapReduce process divide the input data for parallel processing.
Q64.
What is the purpose of the circular memory buffer in a map task?

a.

To store the entire input data

b.

To store the final output data

c.

To continuously write the partial output data

d.

To store metadata about the job

Discuss
Answer: (c).To continuously write the partial output data Explanation:The circular memory buffer in a map task is used to continuously write the partial output data.
Q65.
When does a map task write its partial output data to disk?

a.

After reading the entire input data

b.

As soon as the circular memory buffer is full

c.

When all map tasks are completed

d.

After merging temporary files

Discuss
Answer: (b).As soon as the circular memory buffer is full Explanation:A map task writes its partial output data to disk as soon as the circular memory buffer is full.
Q66.
What is the purpose of the copy phase in reduce tasks?

a.

To copy the map output data to the local disk

b.

To copy the reduce output data to the distributed file system

c.

To request data partitions from nodes where map tasks ran

d.

To sort the map output data

Discuss
Answer: (c).To request data partitions from nodes where map tasks ran Explanation:The copy phase in reduce tasks is used to request data partitions from nodes where map tasks ran.
Q67.
In Hadoop MapReduce, when is the output of the reduce() function written to the distributed file system?

a.

After reading the entire input data

b.

During the copy phase

c.

After merging temporary files

d.

As soon as the reduce task starts

Discuss
Answer: (d).As soon as the reduce task starts Explanation:In Hadoop MapReduce, the output of the reduce() function is written to the distributed file system as soon as the reduce task starts.
Q68.
What does the replication factor in HDFS determine?

a.

The number of splits for input data

b.

The number of nodes in the cluster

c.

The number of copies of each block in HDFS

d.

The number of reducers in a MapReduce job

Discuss
Answer: (c).The number of copies of each block in HDFS Explanation:The replication factor in HDFS determines the number of copies of each block in HDFS for fault tolerance.
Q69.
What is the role of the jobtracker in Hadoop MapReduce?

a.

Managing storage in HDFS

b.

Executing map tasks

c.

Coordinating and scheduling jobs

d.

Storing metadata about the cluster

Discuss
Answer: (c).Coordinating and scheduling jobs Explanation:The jobtracker in Hadoop MapReduce is responsible for coordinating and scheduling jobs.
Q70.
Which phase in a MapReduce job is responsible for sorting the map output data?

a.

Map phase

b.

Copy phase

c.

Shuffle phase

d.

Reduce phase

Discuss
Answer: (c).Shuffle phase Explanation:The shuffle phase in a MapReduce job is responsible for sorting the map output data.
Page 7 of 8

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