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Welcome to the Scalable End User Access to Big Data MCQs Page

Dive deep into the fascinating world of Scalable End User Access to Big Data with our comprehensive set of Multiple-Choice Questions (MCQs). This page is dedicated to exploring the fundamental concepts and intricacies of Scalable End User Access 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 Scalable End User Access 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 Scalable End User Access 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 Scalable End User Access to Big Data. You can click on an option to test your knowledge before viewing the solution for a MCQ. Happy learning!

Scalable End User Access to Big Data MCQs | Page 2 of 14

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Q11.
What are the important aspects of the end-user data access problem?

a.

Size and color

b.

Volume, variety, velocity, and complexity

c.

Speed and efficiency

d.

Accessibility and availability

Discuss
Answer: (b).Volume, variety, velocity, and complexity Explanation:Important aspects of the problem are volume, variety, velocity, and complexity.
Q12.
How does the problem of data access become more challenging in Big Data scenarios?

a.

Data volumes decrease

b.

Data variety decreases

c.

Data velocity decreases

d.

All of the above

Discuss
Answer: (d).All of the above Explanation:In Big Data scenarios, one or more of these dimensions (volume, variety, velocity) can increase, making the problem more challenging.
Q13.
What is the goal of the Optique project regarding data access for Big Data?

a.

To involve more IT experts

b.

To simplify the data access problem

c.

To enable expert end-users to access data without IT experts' help

d.

To create complex data schemas

Discuss
Answer: (c).To enable expert end-users to access data without IT experts' help Explanation:The goal of the Optique project is to enable expert end-users to access data themselves, without the help of IT experts.
Q14.
What does OBDA stand for in the context of data access?

a.

Operational Business Data Access

b.

Ontology-Based Data Access

c.

Online Big Data Analysis

d.

Open Database Architecture

Discuss
Answer: (b).Ontology-Based Data Access Explanation:OBDA stands for Ontology-Based Data Access in the context of data access.
Q15.
How does OBDA address the bottleneck in query translation for end-users?

a.

By eliminating the need for ontologies

b.

By storing data independently of ontologies

c.

By automating the query translation process

d.

By hiring more IT experts

Discuss
Answer: (c).By automating the query translation process Explanation:OBDA can avoid the bottleneck by automating the query translation process.
Q16.
What challenges do end-users typically face when dealing with Big Data?

a.

Lack of data sources

b.

Limited access to IT experts

c.

Slow turnaround time for queries

d.

Small data volumes

Discuss
Answer: (c).Slow turnaround time for queries Explanation:The turnaround time for information requests in dealing with Big Data is often in the range of days or worse.
Q17.
How does the involvement of IT experts affect the turnaround time for information requests?

a.

It speeds up the process

b.

It has no impact on the process

c.

It may delay the process significantly

d.

It reduces the need for data access

Discuss
Answer: (c).It may delay the process significantly Explanation:Collaboration with IT experts can lead to delays in the turnaround time.
Q18.
What dimensions of data make data access challenging in Big Data scenarios?

a.

Color and size

b.

Volume, variety, velocity, and complexity

c.

Width and height

d.

Brightness and contrast

Discuss
Answer: (b).Volume, variety, velocity, and complexity Explanation:In Big Data scenarios, these dimensions can make data access challenging.
Q19.
What is the main idea behind ontology-based data access (OBDA)?

a.

To store data in a single format

b.

To eliminate end-users' information needs

c.

To use ontologies to formalize data storage

d.

To automate the translation of end-user queries

Discuss
Answer: (d).To automate the translation of end-user queries Explanation:The main idea of OBDA is to automate the translation of end-user queries using ontologies.
Q20.
Who typically formulates queries using the terms defined by the ontology in OBDA?

a.

IT experts

b.

Data sources

c.

End-users

d.

Domain experts

Discuss
Answer: (c).End-users Explanation:In OBDA, end-users can formulate queries using the terms defined by the ontology.
Page 2 of 14

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