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Welcome to the Semantic Data Interoperability MCQs Page

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

Semantic Data Interoperability MCQs | Page 1 of 9

Explore more Topics under Big Data Computing

Q1.
What are the three common characteristics of Big Data?

a.

Volume, velocity, and variety

b.

Size, speed, and source

c.

Complexity, velocity, and diversity

d.

Quantity, frequency, and type

Discuss
Answer: (a).Volume, velocity, and variety Explanation:Big Data is commonly characterized by its volume, velocity, and variety, meaning it involves large amounts of data of different types, is time-sensitive, and encompasses diverse data sources.
Q2.
Which organization reported that enterprises and consumers stored massive amounts of data in 2010?

a.

Gartner

b.

McKinsey Global Institute

c.

Manyika et al.

d.

Harmelen

Discuss
Answer: (b).McKinsey Global Institute Explanation:The report on Big Data storage in 2010 was published by McKinsey Global Institute.
Q3.
What is the primary factor that determines the volume of data considered as Big Data?

a.

Data complexity

b.

Data source

c.

Field-specific standards

d.

Data size

Discuss
Answer: (d).Data size Explanation:The volume of data considered as Big Data is primarily determined by its size, which can vary based on the specific field.
Q4.
Which characteristic of Big Data is crucial for applications like fraud detection?

a.

Volume

b.

Velocity

c.

Variety

d.

Complexity

Discuss
Answer: (b).Velocity Explanation:Velocity is crucial for applications like fraud detection, where real-time analysis of large volumes of data is needed.
Q5.
What does the term "semantic data interoperability" refer to?

a.

The ability of systems to exchange data

b.

The ability of systems to interpret the meaning of exchanged data

c.

The process of exchanging data between systems

d.

The integration of data from various sources

Discuss
Answer: (b).The ability of systems to interpret the meaning of exchanged data Explanation:Semantic data interoperability refers to the ability of systems to automatically and accurately interpret the meaning of exchanged data.
Q6.
What are the shared explicit models used for achieving semantic data interoperability called?

a.

Data sources

b.

Models

c.

Ontologies

d.

Schemas

Discuss
Answer: (c).Ontologies Explanation:Shared explicit models used for achieving semantic data interoperability are called ontologies.
Q7.
Which knowledge representation language is considered more expressive for semantic interoperability than RDF?

a.

OWL DL

b.

XML

c.

SQL

d.

JSON

Discuss
Answer: (a).OWL DL Explanation:OWL DL is considered more expressive for semantic interoperability compared to RDF.
Q8.
What does RDF stand for in the context of knowledge representation?

a.

Resource Description Framework

b.

Real Data Format

c.

Rapid Data Fusion

d.

Relational Data Framework

Discuss
Answer: (a).Resource Description Framework Explanation:RDF stands for Resource Description Framework, which is used for knowledge representation.
Q9.
What is the relationship between data interoperability and process interoperability?

a.

Data interoperability is not related to process interoperability.

b.

Data interoperability is a prerequisite for process interoperability.

c.

Process interoperability is a prerequisite for data interoperability.

d.

Data interoperability and process interoperability are independent of each other.

Discuss
Answer: (b).Data interoperability is a prerequisite for process interoperability. Explanation:Data interoperability is considered a precondition for process interoperability.
Q10.
What is the primary challenge addressed when achieving semantic interoperability of data from heterogeneous sources in the context of Big Data?

a.

Volume of data

b.

Variety of data

c.

Velocity of data

d.

Complexity of data

Discuss
Answer: (b).Variety of data Explanation:Achieving semantic interoperability of data from heterogeneous sources in the context of Big Data primarily addresses the variety of data.
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