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Welcome to the Management of Big Semantic Data MCQs Page

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

Management of Big Semantic Data MCQs | Page 1 of 15

Explore more Topics under Big Data Computing

Q1.
What was the new paradigm that emerged to help scientists deal with complex phenomena that couldn't be addressed by analytical models?

a.

Theoretical modeling

b.

Experimental observation

c.

Computational simulation

d.

Data visualization

Discuss
Answer: (c).Computational simulation Explanation:Computational simulation became a new paradigm allowing scientists to address complex phenomena and generate large amounts of data.
Q2.
What are some examples of advanced exploration instruments that produced increasing amounts of data in science?

a.

Telescopes

b.

Microscopes

c.

Thermometers

d.

Calipers

Discuss
Answer: (a).Telescopes Explanation:Telescopes and particle colliders are examples of advanced exploration instruments that generated large amounts of data in science.
Q3.
What does eScience encompass in the scientific data workflow?

a.

Data encoding

b.

Data visualization

c.

Data analysis

d.

All of the above

Discuss
Answer: (d).All of the above Explanation:eScience encompasses the entire scientific data workflow, including data encoding, visualization, and analysis.
Q4.
Where else besides science does the data deluge phenomenon apply?

a.

Only in business

b.

Only in social networks

c.

In various fields including business, government, and everyday life

d.

Nowhere else

Discuss
Answer: (c).In various fields including business, government, and everyday life Explanation:The data deluge phenomenon applies to various fields, including business, government, and everyday life, not just in science.
Q5.
What is referred to as the "new raw material of business" in the context of the data deluge?

a.

Data encoding

b.

Data representation

c.

Data science

d.

Data sets

Discuss
Answer: (d).Data sets Explanation:Data is referred to as the "new raw material of business" in the context of the data deluge.
Q6.
Besides business, what other entity is contributing significantly to the data deluge?

a.

Social networks

b.

Government agencies

c.

Scientific organizations

d.

Educational institutions

Discuss
Answer: (b).Government agencies Explanation:Government agencies, as part of the Open Government movement, are also significant contributors to the data deluge.
Q7.
What is data science primarily focused on?

a.

Data generation

b.

Data visualization

c.

Transforming data into knowledge

d.

Data storage

Discuss
Answer: (c).Transforming data into knowledge Explanation:Data science is primarily focused on transforming data into knowledge.
Q8.
What is a key requirement for acquiring knowledge through data science?

a.

Effective data linkage

b.

Data encryption

c.

Data storage

d.

Data replication

Discuss
Answer: (a).Effective data linkage Explanation:Acquiring knowledge through data science strongly depends on effective data linkage, which allows computers to integrate data from heterogeneous data sets.
Q9.
What is essential for the semiautomatic processing of data in the current data revolution?

a.

Human intervention

b.

Standardized data encoding

c.

Manual data entry

d.

Data encryption

Discuss
Answer: (b).Standardized data encoding Explanation:Semiautomatic processing of data in the data revolution requires standardized data encoding to enable computer exchange and understanding.
Q10.
Why is a standardized data encoding crucial for computer understanding?

a.

It simplifies data storage.

b.

It enables data replication.

c.

It allows computers to resolve the meaning of data.

d.

It prevents data exchange.

Discuss
Answer: (c).It allows computers to resolve the meaning of data. Explanation:A standardized data encoding is crucial because it allows computers to resolve the meaning of data, such as identifying what a gene, galaxy, or temperature measurement is.
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