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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 18 of 43

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Q171.
How does the age of assertions in a KO's knowledge body affect its fitness?

a.

Young assertions make the KO more resistant to stochastic changes.

b.

Only long-living assertions indicate a KO is in the right context.

c.

A mix of young and old assertions indicates high fitness.

d.

Old assertions make the KO more resistant to mutagens.

Discuss
Answer: (c).A mix of young and old assertions indicates high fitness. Explanation:A good mix of young and old assertions in the body of a KO indicates high fitness as it suggests the knowledge is overall valid and evolves appropriately.
Q172.
What is the proportion between the parts of knowledge tokens that influences a KO's fitness?

a.

The number of assertions it can excrete.

b.

The source of the knowledge token.

c.

The size and complexity of the knowledge token.

d.

The number of assertions it can consume versus excrete.

Discuss
Answer: (d).The number of assertions it can consume versus excrete. Explanation:The fitness of a KO depends on the proportion between the parts of knowledge tokens it can consume versus excrete.
Q173.
What property of a knowledge token can affect its value?

a.

The complexity of its TBox.

b.

The source from which it originates.

c.

The number of concepts and properties in its ABox.

d.

The number of environmental contexts it is attributed to.

Discuss
Answer: (b).The source from which it originates. Explanation:The source from which a knowledge token originates can affect its value.
Q174.
Why does a token become less valuable over its lifetime in the environment?

a.

It loses its complexity.

b.

Its source changes.

c.

Its frequency in the context decreases.

d.

It becomes more relevant to other contexts.

Discuss
Answer: (c).Its frequency in the context decreases. Explanation:A token becomes less valuable over its lifetime in the environment because its value decreases as the frequency of similar tokens in the context decreases.
Q175.
How can the quality of an ontology be assessed as a dynamic optimization problem?

a.

By considering the number of KOs in the ecosystem.

b.

By examining the source of knowledge tokens.

c.

By integrating different fitness and value factors.

d.

By focusing on the age of assertions in KOs.

Discuss
Answer: (c).By integrating different fitness and value factors. Explanation:The quality of an ontology can be assessed as a dynamic optimization problem by integrating different fitness and value factors.
Q176.
What is the essence of adequate understanding of the changes in the world reflected in Big Data?

a.

Extracting raw information from Big Data.

b.

Balancing between effectiveness and efficiency.

c.

Petabytes of raw information.

d.

Dissolving the world in data tokens.

Discuss
Answer: (b).Balancing between effectiveness and efficiency. Explanation:Adequate understanding of the changes in the world reflected in Big Data is about balancing between effectiveness and efficiency.
Q177.
What is Big Data considered to be a fine-grained reflection of?

a.

Industrial landscapes.

b.

The state of the art.

c.

Changes in the world.

d.

Petabytes of information.

Discuss
Answer: (c).Changes in the world. Explanation:Big Data is considered to be a fine-grained reflection of the changes happening in the world.
Q178.
Why is the demand for effective and efficient use of Big Data in industries enormous?

a.

Because industries generate vast amounts of data.

b.

Because state-of-the-art technologies fully satisfy this demand.

c.

Because the influence of Big Data on industrial landscapes is minimal.

d.

Because the current state of technology doesn't fully meet this demand.

Discuss
Answer: (d).Because the current state of technology doesn't fully meet this demand. Explanation:The demand for effective and efficient use of Big Data in industries is enormous because the current state of technology doesn't fully meet this demand.
Q179.
What approach was offered to help understand Big Data in a way that balances effectiveness and efficiency?

a.

Analyzing the state of research activities.

b.

Extracting raw information from Big Data.

c.

Dissolving the world in data tokens.

d.

Balancing between effectiveness and efficiency.

Discuss
Answer: (d).Balancing between effectiveness and efficiency. Explanation:The approach offered to help understand Big Data in a way that balances effectiveness and efficiency is to focus on balancing between effectiveness and efficiency in understanding Big Data.
Q180.
What is one of the major recommendations for achieving a balance in processing data semantics?

a.

Following a mechanistic approach to scalability.

b.

Combining top-down and bottom-up processing of data semantics.

c.

Focusing solely on top-down processing.

d.

Avoiding natural incremental and evolutionary approaches.

Discuss
Answer: (b).Combining top-down and bottom-up processing of data semantics. Explanation:One of the major recommendations for achieving a balance in processing data semantics is to intelligently combine top-down and bottom-up processing of data semantics.
Page 18 of 43

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