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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 7 of 43

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Q61.
What is a way to reduce the overheads of the top-down path in Big Data processing?

a.

Efficiently performing simple scalable queries

b.

Decreasing the volume of data

c.

Enhancing the top-down query answering framework

d.

Materializing query results

Discuss
Answer: (d).Materializing query results Explanation:Query results may be materialized to reduce computational overhead in the top-down path.
Q62.
What is the high-level approach for treating Big Data along the proposed path?

a.

3F + 3Co

b.

3D + 3R

c.

3S + 3E

d.

3P + 3Q

Discuss
Answer: (a).3F + 3Co Explanation:The high-level approach for treating Big Data is represented as "3F + 3Co," which stands for Focusing-Filtering-Forgetting and Contextualizing-Compressing-Connecting.
Q63.
What does the "3F" in the formula "3F + 3Co" primarily refer to in the context of the Big Data?

a.

Enhancing knowledge evolution

b.

Executives' multitasking

c.

Knowledge fragmentation

d.

Information overload management

Discuss
Answer: (d).Information overload management Explanation:In the context of the Big Data, "3F" primarily refers to Information overload management, where executives are advised to focus, filter, and forget to deal with the problem.
Q64.
What is one of the key purposes of the proposed bottom-up path in Big Data processing?

a.

Complex query execution

b.

Evolution of knowledge

c.

Data compression

d.

Streamlining top-down queries

Discuss
Answer: (b).Evolution of knowledge Explanation:One of the key purposes of the proposed bottom-up path is the evolution of knowledge in line with data changes.
Q65.
Why is the order of focusing considered important when processing Big Data or its semantics?

a.

It influences the appearance of data tokens.

b.

It affects the efficiency of processing.

c.

It determines the source of data tokens.

d.

It enhances data persistence.

Discuss
Answer: (b).It affects the efficiency of processing. Explanation:The order of focusing is considered important because it affects the efficiency of processing when dealing with Big Data or its semantics.
Q66.
What is a potential advantage of using smart focusing when processing data tokens in a persistent storage?

a.

It guarantees the completeness of data processing.

b.

It eliminates the need for real-time processing.

c.

It can lead to faster token extraction.

d.

It ensures the accuracy of data analysis.

Discuss
Answer: (c).It can lead to faster token extraction. Explanation:Smart focusing in a persistent storage can lead to faster token extraction, allowing for more efficient data processing.
Q67.
What is a challenge associated with smart focusing in data processing?

a.

It requires prior knowledge of token locations.

b.

It may lead to suboptimal solutions.

c.

It depends on real-time data availability.

d.

It eliminates the need for decision-making.

Discuss
Answer: (b).It may lead to suboptimal solutions. Explanation:A challenge with smart focusing in data processing is that it may lead to suboptimal solutions because some potentially valid alternatives are inevitably lost after each choice made on the decision path.
Q68.
What is the main purpose of filtering in data analysis?

a.

To increase the volume of data.

b.

To include all available data.

c.

To remove irrelevant or noisy data.

d.

To focus on multiple dimensions of data.

Discuss
Answer: (c).To remove irrelevant or noisy data. Explanation:The main purpose of filtering in data analysis is to remove irrelevant or noisy data.
Q69.
Why is it challenging to have a one-size-fits-all filter for big heterogeneous data?

a.

Because it requires a decrease in efficiency.

b.

Because it increases data variety/complexity.

c.

Because data tokens cannot be processed efficiently.

d.

Because data filtering depends on the specific context.

Discuss
Answer: (d).Because data filtering depends on the specific context. Explanation:It is challenging to have a one-size-fits-all filter for big heterogeneous data because data filtering depends on the specific context, and there is no single filter that can apply universally.
Q70.
What is a potential benefit of using smart filtering in data analysis?

a.

It guarantees the inclusion of all available data.

b.

It eliminates the need for context-specific filters.

c.

It reduces processing effort while maintaining result quality.

d.

It increases the complexity of data analysis.

Discuss
Answer: (c).It reduces processing effort while maintaining result quality. Explanation:A potential benefit of using smart filtering in data analysis is that it reduces processing effort while maintaining the quality of results.
Page 7 of 43

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