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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 19 of 43

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Q181.
What approach is recommended for processing Big Data and its semantics?

a.

Mechanistic approach to scalability.

b.

Incremental and evolutionary processing.

c.

Top-down processing only.

d.

Traditional database scaling.

Discuss
Answer: (b).Incremental and evolutionary processing. Explanation:The recommended approach for processing Big Data and its semantics is to use a natural incremental and evolutionary way of processing instead of a mechanistic approach to scalability.
Q182.
What do the genomes and bodies of Knowledge Organisms (KOs) describe?

a.

Environmental contexts.

b.

Traditional databases.

c.

Mechanistic approaches.

d.

Data semantics.

Discuss
Answer: (a).Environmental contexts. Explanation:The genomes and bodies of Knowledge Organisms (KOs) describe environmental contexts.
Q183.
How do ontologies evolve in the proposed ecosystem of environmental contexts?

a.

Mechanistically.

b.

In isolation.

c.

In parallel with the evolution of KOs.

d.

They remain static.

Discuss
Answer: (c).In parallel with the evolution of KOs. Explanation:Ontologies evolve in parallel with the evolution of Knowledge Organisms (KOs) in the proposed ecosystem of environmental contexts.
Q184.
What are the four characteristics that characterize Big Data problems?

a.

Volume, variability, visualization, and velocity.

b.

Volume, velocity, variety, and variability.

c.

Velocity, verification, value, and volume.

d.

Visualization, volume, variety, and validation.

Discuss
Answer: (b).Volume, velocity, variety, and variability. Explanation:The four characteristics that characterize Big Data problems are volume, velocity, variety, and variability.
Q185.
Which V refers to the large amount of data generated by individuals, groups, and organizations?

a.

Variety

b.

Velocity

c.

Volume

d.

Variability

Discuss
Answer: (c).Volume Explanation:The V that refers to the large amount of data generated by individuals, groups, and organizations is "volume."
Q186.
Why is the concept of variability important in Big Data analysis?

a.

It refers to the large amount of data.

b.

It describes the variety of data types.

c.

It indicates the speed at which data are collected.

d.

It deals with data unpredictability and changing interpretations.

Discuss
Answer: (d).It deals with data unpredictability and changing interpretations. Explanation:The concept of variability in Big Data analysis is important because it deals with data unpredictability and changing interpretations.
Q187.
What is one of the challenges in managing Big Data, especially in the field of science?

a.

Lack of data sources

b.

Language barriers

c.

Overwhelming computational power

d.

Policy guidelines

Discuss
Answer: (b).Language barriers Explanation:One of the challenges in managing Big Data, especially in the field of science, is overcoming limitations related to language, methodology, and guidelines (policy).
Q188.
Why is it difficult to identify unique architectures and solutions adaptable to all possible applicative areas in Big Data?

a.

Because there are too few application areas.

b.

Because the range of problems and specific needs varies greatly.

c.

Because there is a lack of available tools and techniques.

d.

Because developers prefer a one-size-fits-all approach.

Discuss
Answer: (b).Because the range of problems and specific needs varies greatly. Explanation:It is difficult to identify unique architectures and solutions adaptable to all possible applicative areas in Big Data because the range of problems and specific needs varies greatly.
Q189.
What makes it almost impossible to identify unique architectures and solutions for Big Data?

a.

The lack of skilled developers.

b.

The wide range of problems and specific needs.

c.

The abundance of available data.

d.

The simplicity of Big Data applications.

Discuss
Answer: (b).The wide range of problems and specific needs. Explanation:The wide range of problems and specific needs in different application areas make it almost impossible to identify unique architectures and solutions for Big Data.
Q190.
How can Big Data technologies revolutionize education?

a.

By providing accurate assessments of students' progress.

b.

By replacing traditional teaching methods.

c.

By increasing the complexity of educational data.

d.

By eliminating the need for teachers.

Discuss
Answer: (a).By providing accurate assessments of students' progress. Explanation:Big Data technologies can revolutionize education by providing accurate assessments of students' progress and improving the understanding of students' knowledge.
Page 19 of 43

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