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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 6 of 43

Explore more Topics under Big Data Computing

Q51.
What does the PlanetData project aim to establish in the field of Big Data?

a.

A virtual laboratory for research and experimentation

b.

A sustainable European community of researchers

c.

A data federation platform for handling structured information

d.

A platform for decision support in logistics operations

Discuss
Answer: (b).A sustainable European community of researchers Explanation:The PlanetData project aims to establish a sustainable European community of researchers in the field of Big Data.
Q52.
Which project focuses on developing a scalable and adaptive environment for knowledge discovery from Earth Observation images and geospatial data sets?

a.

Khresmoi

b.

Robust

c.

Teleios

d.

Fish4Knowledge

Discuss
Answer: (c).Teleios Explanation:The Teleios project focuses on developing a scalable and adaptive environment for knowledge discovery from Earth Observation images and geospatial data sets.
Q53.
What is the primary role of knowledge in the context of Big Data?

a.

Knowledge is irrelevant in the context of Big Data.

b.

Knowledge is generated by each generation to support decision-making.

c.

Knowledge is primarily used for data storage.

d.

Knowledge is the raw data collected over generations.

Discuss
Answer: (b).Knowledge is generated by each generation to support decision-making. Explanation:Knowledge is produced by each generation for their needs and is used to support decision-making.
Q54.
What is the main objective of adding the semantics layer to the Big Data processing stack?

a.

To reduce the volume of data.

b.

To improve data annotation.

c.

To create new data silos.

d.

To achieve more effective use of semantics.

Discuss
Answer: (d).To achieve more effective use of semantics. Explanation:The semantics layer is added to achieve more effective use of semantics in Big Data processing.
Q55.
What is the consequence of the lack of rational incentive for investing in ontology reuse?

a.

Increased interoperability and integration.

b.

Creation of a "Big Ontology" challenge.

c.

Rapid development of semantic technologies.

d.

Efficient knowledge management at scale.

Discuss
Answer: (b).Creation of a "Big Ontology" challenge. Explanation:The lack of rational incentive for ontology reuse leads to the creation of a "Big Ontology" challenge.
Q56.
What is the primary consequence of adding a data semantics layer to Big Data processing?

a.

Increased efficiency and decreased complexity.

b.

Decreased computational overhead.

c.

Increased computational efficiency.

d.

Increased computational complexity and decreased efficiency.

Discuss
Answer: (d).Increased computational complexity and decreased efficiency. Explanation:Adding a data semantics layer increases effectiveness but also substantially increases computational complexity, leading to decreased efficiency.
Q57.
What is one of the shortcomings of the unidirectional approach to developing the Big Data semantics layer?

a.

Inadequate scalability

b.

Consistency issues with ontologies

c.

Inefficient query execution

d.

Lack of knowledge repository

Discuss
Answer: (c).Inefficient query execution Explanation:Scalability overhead is a shortcoming of the unidirectional approach, leading to inefficient query execution.
Q58.
What is the primary challenge when ontologies are inconsistent with data?

a.

Lack of query planning interfaces

b.

Volume of changes in ontologies

c.

Effectiveness problems

d.

Lack of completeness in data

Discuss
Answer: (c).Effectiveness problems Explanation:Inconsistency between ontologies and data leads to effectiveness problems.
Q59.
What is the advantage of an approach that combines top-down query answering with bottom-up ontology evolution?

a.

Reduced computational overhead

b.

Elimination of scalability issues

c.

Prescribed ontologies in advance

d.

Dynamic discovery of evolving ontologies

Discuss
Answer: (d).Dynamic discovery of evolving ontologies Explanation:Such an approach allows for the dynamic discovery of evolving ontologies without having to prescribe them in advance.
Q60.
What is the primary characteristic of the proposed bottom-up path in the Big Data processing stack?

a.

Efficiently performing complex queries

b.

Decreasing volumes of data

c.

Evolution of knowledge

d.

Top-down query answering

Discuss
Answer: (c).Evolution of knowledge Explanation:The proposed bottom-up path is characterized by the evolution of knowledge in line with data changes.
Page 6 of 43

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