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Welcome to the Introduction to Big Data MCQs Page

Dive deep into the fascinating world of Introduction to Big Data with our comprehensive set of Multiple-Choice Questions (MCQs). This page is dedicated to exploring the fundamental concepts and intricacies of Introduction to Big Data, a crucial aspect of Big Data Computing. In this section, you will encounter a diverse range of MCQs that cover various aspects of Introduction to Big Data, 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 Introduction to Big Data. 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 Introduction to Big Data. You can click on an option to test your knowledge before viewing the solution for a MCQ. Happy learning!

Introduction to Big Data MCQs | Page 1 of 43

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Q1.
What is one significant factor contributing to the exponential growth of data volumes?

a.

Decreased use of social networking applications

b.

Inadequate storage capacity

c.

Proliferation of mobile devices

d.

Limited data generation by nonspecialist users

Discuss
Answer: (c).Proliferation of mobile devices Explanation:The exponential growth of data volumes is accelerated by the proliferation of rapidly evolving mobile devices.
Q2.
What is the role of social networking applications in the exponential growth of data volumes?

a.

They limit data generation by users.

b.

They have no impact on data volumes.

c.

They facilitate easy and free content creation by nonspecialist users.

d.

They prioritize data storage over data generation.

Discuss
Answer: (c).They facilitate easy and free content creation by nonspecialist users. Explanation:Social networking applications facilitate easy and free content creation by nonspecialist users, contributing to the exponential growth of data volumes.
Q3.
What is the primary challenge associated with mined correlations in Big Data analysis?

a.

Lack of historical relevance

b.

Inability to answer "why" questions

c.

Complexity of the data

d.

Limited applicability to industrial applications

Discuss
Answer: (b).Inability to answer "why" questions Explanation:Mined correlations are useful for "what" questions but not for "why" questions, indicating a primary challenge.
Q4.
What are the two key factors for effective Big Data analysis?

a.

Completeness and granularity

b.

Complexity and efficiency

c.

Timeliness and relevance

d.

Automation and manual analysis

Discuss
Answer: (a).Completeness and granularity Explanation:For effective Big Data analysis, data should be complete, and facts should be faceted adequately for further inference.
Q5.
What additional complication is associated with managing Big Knowledge?

a.

Extracting knowledge from Big Data

b.

Resolving contradictory opinions

c.

Handling source data efficiently

d.

Increasing data completeness

Discuss
Answer: (b).Resolving contradictory opinions Explanation:Managing Big Knowledge involves handling contradictory and changing opinions, presenting an additional complication.
Q6.
What role is emphasized in managing the authority and reputation of "experts" in the context of Big Data?

a.

Developing automated algorithms

b.

Refining the data storage layer

c.

Collecting more data

d.

Refining the semantic layer

Discuss
Answer: (d).Refining the semantic layer Explanation:Managing the authority and reputation of "experts" involves refining the semantic layer in Big Data processing.
Q7.
What is the primary goal of big data analytics?

a.

Generating terabytes of low-value data

b.

Collecting vast amounts of data

c.

Distilling data to a single bit of high-value data

d.

Understanding market conditions

Discuss
Answer: (c).Distilling data to a single bit of high-value data Explanation:The goal of big data analytics is to distill terabytes of low-value data down to, in some cases, a single bit of high-value data.
Q8.
What is the key requirement for making Big Data healthy and understandable for analytics?

a.

Collecting more data

b.

Using advanced analytics tools

c.

Ensuring effectiveness and efficiency

d.

Expanding digital storage capacity

Discuss
Answer: (c).Ensuring effectiveness and efficiency Explanation:Making Big Data healthy and understandable for analytics requires ensuring effectiveness and efficiency.
Q9.
What is considered a deciding factor by today's business analytics?

a.

The use of advanced analytics tools

b.

The amount of data collected

c.

Turning available Big Data assets into action and performance

d.

The use of the Apache Hadoop framework

Discuss
Answer: (c).Turning available Big Data assets into action and performance Explanation:Turning available Big Data assets into action and performance is considered a deciding factor by today's business analytics.
Q10.
What are some of the perceived benefits of harnessing Big Data for decision-making?

a.

Increased digital storage capacity

b.

Better risk management

c.

Use of advanced analytics tools

d.

Expanded market presence

Discuss
Answer: (b).Better risk management Explanation:There are several perceived benefits of harnessing Big Data for decision-making, including better risk management.
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