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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 2 of 15

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Q11.
What are gaining momentum as practical trends for data representation in the context of Big Semantic Data?

a.

Images and videos

b.

Graph-oriented representations and rich semantic vocabularies

c.

Text-based representations

d.

Audio recordings

Discuss
Answer: (b).Graph-oriented representations and rich semantic vocabularies Explanation:Graph-oriented representations and rich semantic vocabularies are gaining momentum as practical trends for data representation in the context of Big Semantic Data.
Q12.
What does the Resource Description Framework (RDF) serve as in the context of data encoding and semantic technologies?

a.

A data storage format

b.

A standardized query language

c.

A model for data encoding and semantic technologies

d.

A web browser

Discuss
Answer: (c).A model for data encoding and semantic technologies Explanation:The Resource Description Framework (RDF) serves as a standard model for data encoding and semantic technologies for the publication, exchange, and consumption of Big Semantic Data.
Q13.
What are the potential improvements achieved by HDT in Big Semantic Data management?

a.

Improved data replication

b.

Enhanced data encryption

c.

Scalability improvements for storage, exchange, and query answering

d.

Reduced data variety

Discuss
Answer: (c).Scalability improvements for storage, exchange, and query answering Explanation:HDT allows scalability improvements to be achieved for storage, exchange, and query answering in Big Semantic Data management.
Q14.
What are the three main dimensions of Big Data?

a.

Variety, veracity, and viability

b.

Volume, velocity, and variety

c.

Validity, velocity, and value

d.

Volume, value, and veracity

Discuss
Answer: (b).Volume, velocity, and variety Explanation:The three main dimensions of Big Data are volume, velocity, and variety.
Q15.
Which dimension of Big Data is described as the challenge related to streaming data processing?

a.

Volume

b.

Velocity

c.

Variety

d.

Validity

Discuss
Answer: (b).Velocity Explanation:The dimension of Big Data related to the challenge of streaming data processing is velocity.
Q16.
What does the variety dimension of Big Data refer to?

a.

The number of data sources

b.

The degree of data duplication

c.

The various degrees of structure within the source data

d.

The speed at which data is generated

Discuss
Answer: (c).The various degrees of structure within the source data Explanation:The variety dimension of Big Data refers to the various degrees of structure (or lack thereof) within the source data.
Q17.
What is the main challenge of Big Data variety?

a.

Achieving data integration

b.

Managing data volume

c.

Ensuring data veracity

d.

Optimizing data retrieval

Discuss
Answer: (a).Achieving data integration Explanation:The main challenge of Big Data variety is achieving effective data integration.
Q18.
How can data value be increased in Big Data processing?

a.

By reducing data volume

b.

By slowing down data velocity

c.

By using semantic technologies to address data variety

d.

By limiting the sources of data

Discuss
Answer: (c).By using semantic technologies to address data variety Explanation:Data value in Big Data processing can be increased by using semantic technologies to address data variety.
Q19.
What is the primary goal of the Semantic Web?

a.

To make information on the WWW machine-processable

b.

To increase the volume of data on the WWW

c.

To improve the speed of data retrieval on the WWW

d.

To enhance the current WWW by incorporating machine-processable semantics

Discuss
Answer: (d).To enhance the current WWW by incorporating machine-processable semantics Explanation:The primary goal of the Semantic Web is to enhance the current WWW by incorporating machine-processable semantics to information objects.
Q20.
What distinguishes the approach of information retrieval techniques from database techniques in the context of the Semantic Web?

a.

Information retrieval techniques use schemas to structure data, while database techniques rely on metadata.

b.

Information retrieval techniques are more efficient for querying data with logical meaning.

c.

Database techniques are based on human feedback, while information retrieval techniques are fully automated.

d.

Database techniques use schemas to structure data, while information retrieval techniques rely on metadata.

Discuss
Answer: (a).Information retrieval techniques use schemas to structure data, while database techniques rely on metadata. Explanation:In the context of the Semantic Web, information retrieval techniques use schemas to structure data, while database techniques rely on metadata to give meaning to data.
Page 2 of 15

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