adplus-dvertising

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.

frame-decoration

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 1 of 8

Explore more Topics under Big Data Computing

Q1.
What is the equivalent of the revolution that time-sharing introduced in the era of batch processing?

a.

Cloud computing

b.

Resource provisioning

c.

Dynamic provisioning

d.

Illusion of unlimited resources

Discuss
Answer: (c).Dynamic provisioning Explanation:The equivalent of the revolution introduced by time-sharing in the era of batch processing is "Dynamic provisioning." Time-sharing allowed users to interact with computers as if they were the owners of the system, and dynamic provisioning aims to free users from thinking about resource provisioning.
Q2.
What is one of the main obstacles to achieving the illusion of the cloud as an unlimited source of computing resources?

a.

Lack of available cloud platforms

b.

Difficulty in maintaining cloud systems

c.

Resource limitations and the need for prioritization

d.

Inefficient lower-level components

Discuss
Answer: (c).Resource limitations and the need for prioritization Explanation:The main obstacle to achieving the illusion of the cloud as an unlimited source of computing resources is " Resource limitations and the need for prioritization." While the cloud provides flexibility, resources are limited, and providers need mechanisms for sharing and prioritizing services.
Q3.
What is the primary objective of the MapReduce programming model?

a.

To generate key-value pairs

b.

To tolerate machine failures

c.

To standardize parallel applications

d.

To process small computations

Discuss
Answer: (c).To standardize parallel applications Explanation:The MapReduce programming model primarily aims to standardize parallel applications, simplifying the development of massively parallel applications.
Q4.
Which two major steps constitute the MapReduce model?

a.

Shuffle and sort

b.

Merge and divide

c.

Map and reduce

d.

Input and output

Discuss
Answer: (c).Map and reduce Explanation:The MapReduce model consists of two major steps: map and reduce, involving data processing through the map and reduce functions.
Q5.
What type of results is generated during the MapReduce process?

a.

Final output results

b.

Key-value pairs

c.

Input data

d.

Intermediate key-value pairs

Discuss
Answer: (d).Intermediate key-value pairs Explanation:The MapReduce process generates intermediate key-value pairs, which are further processed by the reduce function.
Q6.
How does MapReduce ensure scalability and reliability?

a.

By dividing the work into smaller units

b.

By using a centralized master node

c.

By storing all data on a single node

d.

By using a complex interface

Discuss
Answer: (a).By dividing the work into smaller units Explanation:MapReduce ensures scalability and reliability by partitioning the work into smaller units, enabling parallel processing and fault tolerance.
Q7.
What is the role of the master node in MapReduce?

a.

To store input and output files

b.

To apply the map function

c.

To manage the execution of applications in the cluster

d.

To serve intermediate key-value pairs

Discuss
Answer: (c).To manage the execution of applications in the cluster Explanation:The master node in MapReduce is responsible for overseeing the execution of applications in the cluster, including task assignment and coordination.
Q8.
How does MapReduce compare to Relational Database Management Systems (RDBMS)?

a.

MapReduce is the dominant choice for transactional and analytical applications.

b.

MapReduce and RDBMS have a significant overlap in functionality.

c.

MapReduce is more suitable for fine-grained queries compared to RDBMS.

d.

MapReduce and RDBMS are complementary models with different features.

Discuss
Answer: (d).MapReduce and RDBMS are complementary models with different features. Explanation:MapReduce and RDBMS can be seen as complementary models with different features and goals, rather than direct competitors.
Q9.
What is a key difference between MapReduce and RDBMS in terms of data processing?

a.

MapReduce is optimized for interactive access.

b.

RDBMS is suitable for processing semistructured data.

c.

MapReduce processes most of the data set, while RDBMS queries are fine-grained.

d.

RDBMS scales linearly with larger data sets.

Discuss
Answer: (c).MapReduce processes most of the data set, while RDBMS queries are fine-grained. Explanation:MapReduce typically involves processing a significant portion of the data set, whereas RDBMS queries can be more fine-grained.
Q10.
Why is MapReduce considered more suitable for handling larger data sets compared to traditional RDBMS?

a.

MapReduce has lower seek times on hard drives.

b.

MapReduce is optimized for solid-state drives (SSD).

c.

MapReduce scales linearly with cluster size.

d.

Traditional RDBMS offers better data integrity.

Discuss
Answer: (c).MapReduce scales linearly with cluster size. Explanation:MapReduce can scale linearly and handle larger data sets by doubling the size of the cluster, which may not be true for traditional RDBMS.
Page 1 of 8

Suggested Topics

Are you eager to expand your knowledge beyond Big Data Computing? We've curated a selection of related categories that you might find intriguing.

Click on the categories below to discover a wealth of MCQs and enrich your understanding of Computer Science. Happy exploring!

SQL Server

Master Microsoft's database server with our SQL Server MCQs. Topics cover database...

MATLAB

Hone your computational mathematics skills with our MATLAB MCQs. Understand...

Python

Dive into one of the most popular languages with our Python MCQs. Understand...

Systems Programming

Decode low-level programming with our Systems Programming MCQs. These questions delve...

Digital Communication

Master the basics of electronic communication with our Digital Communication MCQs....

Software Architecture and Design

Get a strong hold on the blueprint of software systems with our Software Architecture...

Computer Graphics

Step into the visual side of computing with our Computer Graphics MCQs. Cover topics...

Cyber Security

Understand the fundamentals of safeguarding digital assets with our Cyber Security...

Object Oriented Programming

Enhance your coding skills with our Object Oriented Programming MCQs. Understand...