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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 17 of 43

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Q161.
In competitive scenarios among Knowledge Organisms (KOs), who typically acquires a knowledge token?

a.

The KO with the highest fitness.

b.

The KO with the lowest fitness.

c.

The KO with the fastest migration speed.

d.

The KO with the most genetic drift.

Discuss
Answer: (a).The KO with the highest fitness. Explanation:In competitive scenarios, the strongest KO, often with the highest fitness, is more likely to acquire the knowledge token.
Q162.
What is the primary objective of a Big Data computing system or infrastructure?

a.

Providing a capability for data analysis with effectiveness only.

b.

Providing a capability for data analysis with efficiency only.

c.

Providing a capability for data analysis with balanced effectiveness and efficiency.

d.

Providing a capability for data storage.

Discuss
Answer: (c).Providing a capability for data analysis with balanced effectiveness and efficiency. Explanation:The primary objective of a Big Data computing system is to provide a capability for data analysis with balanced effectiveness and efficiency.
Q163.
What role do ontologies play in the Knowledge Organism (KO) ecosystem?

a.

They are primarily used for data integration.

b.

They register desired evolutionary changes.

c.

They facilitate data processing layers.

d.

They provide a framework for environmental context.

Discuss
Answer: (b).They register desired evolutionary changes. Explanation:Ontologies in the KO ecosystem register desired evolutionary changes and are a fundamental part of the ecosystem.
Q164.
How do ontologies change in the evolving ecosystem?

a.

Ontologies are designed beforehand and remain static.

b.

Changes in ontologies are caused by mutagens in incoming data.

c.

Ontologies evolve independently of data processing.

d.

Ontologies change due to deliberate modifications by KOs.

Discuss
Answer: (b).Changes in ontologies are caused by mutagens in incoming data. Explanation:Changes in ontologies are caused by the mutagens brought by incoming data, and they evolve in parallel to data processing.
Q165.
What does "reuse" of an ontology in the context of KOs and data processing mean?

a.

Using the same ontology for different species of KOs.

b.

Integrating all assertions from knowledge tokens seamlessly.

c.

Describing data about similar things using the same ontological fragments.

d.

Recycling ontologies for new environmental contexts.

Discuss
Answer: (c).Describing data about similar things using the same ontological fragments. Explanation:Reuse of an ontology means that data about similar things is described using the same ontological fragments, promoting interoperability.
Q166.
How does the reuse of ontologies increase their value in the KO ecosystem?

a.

It reduces the need for excreting knowledge tokens.

b.

It makes ontologies evolve independently of data.

c.

It ensures ontologies remain static over time.

d.

It simplifies the process of knowledge extraction.

Discuss
Answer: (a).It reduces the need for excreting knowledge tokens. Explanation:Reuse of ontologies allows for the seamless integration of new knowledge tokens, reducing the need to excrete parts of them.
Q167.
In the evolving KO ecosystem, what leads to changes in ontologies?

a.

Deliberate modifications by KOs.

b.

Environmental changes in habitat.

c.

Mutagens brought by incoming data.

d.

Reproduction and recombination among KOs.

Discuss
Answer: (c).Mutagens brought by incoming data. Explanation:Changes in ontologies in the evolving KO ecosystem are primarily caused by the mutagens brought by incoming data.
Q168.
What is the purpose of the knowledge extraction subsystem in the KO ecosystem?

a.

To create ontologies from scratch.

b.

To prepare ontologies for seamless integration.

c.

To transform data into knowledge tokens.

d.

To facilitate ontology reuse.

Discuss
Answer: (c).To transform data into knowledge tokens. Explanation:The knowledge extraction subsystem transforms units of data into knowledge tokens in the KO ecosystem.
Q169.
How do ontologies naturally change in the KO ecosystem?

a.

Through deliberate interventions by external agents.

b.

By remaining static and unaffected by data processing.

c.

Through seamless integration of new knowledge tokens.

d.

By discarding existing ontologies and replacing them.

Discuss
Answer: (c).Through seamless integration of new knowledge tokens. Explanation:Ontologies in the KO ecosystem change naturally through the seamless integration of new knowledge tokens.
Q170.
What is one of the factors that can influence the fitness of a KO in the ecosystem?

a.

The number of KOs in the same species.

b.

The age of assertions in the KO's knowledge body.

c.

The complexity of the KO's genome.

d.

The frequency of excreted knowledge tokens.

Discuss
Answer: (b).The age of assertions in the KO's knowledge body. Explanation:The age of assertions in the KO's knowledge body can influence its fitness.
Page 17 of 43

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