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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 20 of 43

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Q191.
What can Big Data in education help identify regarding students' learning styles?

a.

Students' favorite subjects.

b.

Clusters of students with similar learning styles or difficulties.

c.

The most popular teaching methods.

d.

Students' extracurricular activities.

Discuss
Answer: (b).Clusters of students with similar learning styles or difficulties. Explanation:Big Data in education can help identify clusters of students with similar learning styles or difficulties.
Q192.
How can Big Data improve personalized instruction in education?

a.

By increasing the number of students in a class.

b.

By replacing teachers with computational models.

c.

By providing data-driven insights into students' needs.

d.

By standardizing teaching methods for all students.

Discuss
Answer: (c).By providing data-driven insights into students' needs. Explanation:Big Data can improve personalized instruction in education by providing data-driven insights into students' needs, which can lead to customized instruction.
Q193.
What is the main goal of data analysis in the educational sector?

a.

To eliminate the need for teachers.

b.

To collect as much data as possible.

c.

To provide information to key stakeholders.

d.

To create standardized assessments.

Discuss
Answer: (c).To provide information to key stakeholders. Explanation:The main goal of data analysis in the educational sector is to provide information to key stakeholders, including educators and administrators.
Q194.
How can data mining benefit the e-learning domain?

a.

By eliminating the need for online learning providers.

b.

By storing all personal details of learners.

c.

By enabling the creation of targeted teaching programs.

d.

By increasing the number of learners.

Discuss
Answer: (c).By enabling the creation of targeted teaching programs. Explanation:Data mining can benefit the e-learning domain by enabling the creation of targeted teaching programs based on learners' interests and needs.
Q195.
What is Europeana, and what is its purpose?

a.

Europeana is a new data modeling technique.

b.

Europeana is an e-learning platform.

c.

Europeana is a cultural heritage information database.

d.

Europeana is a social media platform.

Discuss
Answer: (c).Europeana is a cultural heritage information database. Explanation:Europeana is a cultural heritage information database created to manage large amounts of cultural heritage information data. It aims to provide access to cultural content.
Q196.
What is the main challenge posed by data generated by social networks like Facebook and search engines like Google and Yahoo?

a.

The data is not valuable for businesses.

b.

The data is too structured and easy to analyze.

c.

The volume and variability of data flow.

d.

The data is not collected in real time.

Discuss
Answer: (c).The volume and variability of data flow. Explanation:The main challenge posed by data generated by social networks and search engines is the volume and variability of data flow.
Q197.
How can social media and Big Data solutions use data from social networks for analysis?

a.

By estimating user-collective profiles and behavior.

b.

By preventing users from sharing data.

c.

By limiting the number of uploads per month.

d.

By ignoring data from social networks.

Discuss
Answer: (a).By estimating user-collective profiles and behavior. Explanation:Social media and Big Data solutions can use data from social networks to estimate user-collective profiles and behavior, among other things.
Q198.
What can social media data be used for in the context of analyzing urban living?

a.

Analyzing product acceptance.

b.

Assessing the city's infrastructure.

c.

Predicting the stock market.

d.

Tracking user movements on social media.

Discuss
Answer: (b).Assessing the city's infrastructure. Explanation:Social media data can be used to assess the city's infrastructure and analyze urban living, including massive folk movements and activities in the city.
Q199.
What is the potential benefit of using predictive models in healthcare?

a.

Reducing the quality of healthcare.

b.

Increasing the cost of healthcare.

c.

Enhancing the quality of healthcare and lowering its cost.

d.

Creating additional administrative burdens.

Discuss
Answer: (c).Enhancing the quality of healthcare and lowering its cost. Explanation:The use of predictive models in healthcare can enhance the quality of healthcare and lower its cost by supporting triage, diagnosis, and chronic disease management.
Q200.
What is the main aim of the ZENITH project at the University Montpellier?

a.

To explore cloud computing techniques for genetic and neuroimaging data analysis.

b.

To preserve digital scientific data using a scalable preservation environment.

c.

To extract meaning from data in astronomy, sociology, and neuroscience.

d.

To develop a new database system for managing scientific files.

Discuss
Answer: (c).To extract meaning from data in astronomy, sociology, and neuroscience. Explanation:The main aim of the ZENITH project is to extract meaning from data in astronomy, sociology, and neuroscience.
Page 20 of 43

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