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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 6 of 8

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Q51.
What is the key advantage of Hadoop's distributed file system, HDFS?

a.

HDFS is optimized for high-throughput access to application data.

b.

HDFS is a relational database management system.

c.

HDFS is designed for low-latency, real-time data processing.

d.

HDFS is primarily used for scientific computing.

Discuss
Answer: (a).HDFS is optimized for high-throughput access to application data. Explanation:Hadoop's distributed file system, HDFS, is optimized for high-throughput access to application data.
Q52.
What is the primary purpose of the HBase database?

a.

To provide data summarization and ad-hoc querying

b.

To store and process structured data for large tables

c.

To generate MapReduce programs from SQL queries

d.

To provide high-throughput access to log files

Discuss
Answer: (b).To store and process structured data for large tables Explanation:The primary purpose of the HBase database is to store and process structured data for large tables.
Q53.
What is the role of the master node in a Hadoop MapReduce cluster?

a.

Processing user's requests

b.

Executing map and reduce tasks

c.

Distributing and scheduling work on tasktrackers

d.

Reporting status updates to the jobtracker

Discuss
Answer: (c).Distributing and scheduling work on tasktrackers Explanation:The master node in a Hadoop MapReduce cluster is responsible for distributing and scheduling work on tasktrackers.
Q54.
In a MapReduce job, what is the unit of work that users submit to the jobtracker?

a.

Task

b.

Slot

c.

Split

d.

Job

Discuss
Answer: (d).Job Explanation:In a MapReduce job, the unit of work that users submit to the jobtracker is called a "job."
Q55.
How does the jobtracker divide input data in a Hadoop MapReduce cluster?

a.

It creates virtual clusters.

b.

It divides data into slots.

c.

It divides data into virtual splits.

d.

It creates virtual jobs.

Discuss
Answer: (c).It divides data into virtual splits. Explanation:The jobtracker divides input data in a Hadoop MapReduce cluster into virtual splits.
Q56.
What is the default number of map and reduce slots on a tasktracker in Hadoop?

a.

Two map slots and four reduce slots

b.

Four map slots and two reduce slots

c.

Two map slots and two reduce slots

d.

Four map slots and four reduce slots

Discuss
Answer: (c).Two map slots and two reduce slots Explanation:The default number of map slots and reduce slots on a tasktracker in Hadoop is two each.
Q57.
Why does the Hadoop master represent a single point of failure?

a.

Because it has limited processing power

b.

Because it cannot distribute tasks to tasktrackers

c.

Because it is not distributed and can fail, causing cluster issues

d.

Because it does not have access to the jobtracker

Discuss
Answer: (c).Because it is not distributed and can fail, causing cluster issues Explanation:The Hadoop master represents a single point of failure because it is not distributed, and if it fails, it can cause cluster issues.
Q58.
What is the primary design goal of the Hadoop Distributed File System (HDFS)?

a.

Low-latency access

b.

High throughput for streaming data

c.

High fault tolerance

d.

Compatibility with POSIX semantics

Discuss
Answer: (b).High throughput for streaming data Explanation:The primary design goal of Hadoop Distributed File System (HDFS) is high throughput for streaming data.
Q59.
What is the role of the namenode in HDFS?

a.

Storing data blocks

b.

Coordinating read and write requests

c.

Maintaining metadata and file system information

d.

Handling replication of data blocks

Discuss
Answer: (c).Maintaining metadata and file system information Explanation:The namenode in HDFS is responsible for maintaining metadata and file system information.
Q60.
What is the default block size in HDFS?

a.

128 MB

b.

32 MB

c.

64 MB

d.

256 MB

Discuss
Answer: (c).64 MB Explanation:The default block size in HDFS is 64 MB.
Page 6 of 8

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