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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 29 of 43

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Q281.
What is the role of graph databases in Big Data solutions?

a.

They are primarily used for storage purposes.

b.

They are used for complex data modeling and relationship representation.

c.

They are used for indexing and querying large data sets.

d.

They are not relevant to Big Data solutions.

Discuss
Answer: (b).They are used for complex data modeling and relationship representation. Explanation:Graph databases play a role in Big Data solutions by being used for complex data modeling and relationship representation.
Q282.
What is the purpose of faceted results in data access?

a.

To limit the number of search results

b.

To prevent users from filtering search results

c.

To provide a clear view of the overall data structure

d.

To allow users to further restrict search results using facets/filters

Discuss
Answer: (d).To allow users to further restrict search results using facets/filters Explanation:The purpose of faceted results in data access is to allow users to further restrict search results using facets/filters.
Q283.
Why is data security particularly important in Big Data solutions?

a.

Big Data solutions are not vulnerable to data breaches.

b.

Big Data solutions primarily process non-sensitive data.

c.

Big Data solutions often deal with sensitive and confidential information.

d.

Big Data solutions are typically isolated from external threats.

Discuss
Answer: (c).Big Data solutions often deal with sensitive and confidential information. Explanation:Data security is particularly important in Big Data solutions because they often deal with sensitive and confidential information.
Q284.
What is DRM (Digital Rights Management) in the context of data security?

a.

A method for encrypting data for secure storage.

b.

A technique for preventing unauthorized access to data.

c.

A technology that defines and manages rights and access to content.

d.

A form of conditional access control for data.

Discuss
Answer: (c).A technology that defines and manages rights and access to content. Explanation:DRM (Digital Rights Management) is a technology that defines and manages rights and access to content in the context of data security.
Q285.
How does DRM contribute to data privacy in Big Data solutions?

a.

DRM encrypts all data to ensure privacy.

b.

DRM defines access rights for each piece of data.

c.

DRM restricts data access to authorized and authenticated users.

d.

DRM eliminates the need for authentication and authorization.

Discuss
Answer: (c).DRM restricts data access to authorized and authenticated users. Explanation:DRM contributes to data privacy in Big Data solutions by restricting data access to authorized and authenticated users.
Q286.
What is the purpose of Conditional Access Systems (CAS) in data security?

a.

To encrypt all data for maximum protection.

b.

To prevent unauthorized access to data.

c.

To manage and control user access to services and data.

d.

To eliminate the need for authentication.

Discuss
Answer: (c).To manage and control user access to services and data. Explanation:The purpose of Conditional Access Systems (CAS) in data security is to manage and control user access to services and data.
Q287.
In the context of data security, what is meant by "conditional access"?

a.

All data access is strictly controlled.

b.

Access is granted only if certain conditions are met.

c.

Data access is unrestricted.

d.

Access is limited to a single user.

Discuss
Answer: (b).Access is granted only if certain conditions are met. Explanation:In the context of data security, "conditional access" means access is granted only if certain conditions are met.
Q288.
How does data security relate to data privacy in Big Data solutions?

a.

Data security and data privacy are unrelated concepts.

b.

Data security ensures data privacy.

c.

Data privacy is not a concern in Big Data solutions.

d.

Data privacy is the primary focus of data security.

Discuss
Answer: (b).Data security ensures data privacy. Explanation:Data security ensures data privacy in Big Data solutions by protecting data from unauthorized access and breaches.
Q289.
What is the role of encryption in data security?

a.

To provide conditional access control.

b.

To define and manage rights and access to content.

c.

To restrict data access to authorized users.

d.

To protect data from unauthorized access and breaches.

Discuss
Answer: (d).To protect data from unauthorized access and breaches. Explanation:The role of encryption in data security is to protect data from unauthorized access and breaches by encoding it in a secure format.
Q290.
What are some of the tasks that data analytic algorithms perform in Big Data processing?

a.

Data ingestion and crawling.

b.

Data verification and validation.

c.

Pattern recognition and natural language processing.

d.

All of the above.

Discuss
Answer: (d).All of the above. Explanation:Data analytic algorithms in Big Data processing perform various tasks including data ingestion, crawling, verification, validation, pattern recognition, natural language processing, and more.
Page 29 of 43

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