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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 8 of 43

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Q71.
How does the evolutionary approach address the challenge of smart filtering?

a.

By using a fixed system of environmental contexts.

b.

By applying a one-size-fits-all filter.

c.

By adjusting contexts through independent evolutionary mechanisms.

d.

By using a single filter for all types of data.

Discuss
Answer: (c).By adjusting contexts through independent evolutionary mechanisms. Explanation:The evolutionary approach addresses the challenge of smart filtering by adjusting contexts through independent evolutionary mechanisms, allowing for flexibility in filtering based on changing conditions.
Q72.
What property of knowledge organisms in the evolutionary approach helps in filtering out noise?

a.

Resistance to sporadic mutagenic factors.

b.

Fixed contexts for knowledge collection.

c.

Limited migration between contexts.

d.

Lack of adjustment mechanisms.

Discuss
Answer: (a).Resistance to sporadic mutagenic factors. Explanation:Resistance to sporadic mutagenic factors in knowledge organisms helps in filtering out noise by reducing the impact of disruptive factors on the filtering process.
Q73.
Why is it challenging to decide which part of a potentially useful collection of data should be sacrificed or "forgotten"?

a.

Older records are always less valuable.

b.

Forgotten features are rarely needed in the future.

c.

Some records may have minimal potential utility.

d.

Fixed lifetime policies are ineffective.

Discuss
Answer: (c).Some records may have minimal potential utility. Explanation:It is challenging to decide which part of a potentially useful collection of data should be sacrificed or "forgotten" because some records may have minimal potential utility, and their value cannot be easily assessed.
Q74.
What is the drawback of following straightforward policies like fixed lifetime for keeping records in Big Data management?

a.

It leads to the preservation of all data.

b.

It causes regret almost inevitably.

c.

It ensures data preservation.

d.

It simplifies data management.

Discuss
Answer: (b).It causes regret almost inevitably. Explanation:The drawback of following straightforward policies like a fixed lifetime for keeping records in Big Data management is that it causes regret almost inevitably when valuable data is discarded prematurely.
Q75.
What is one potentially viable approach to forgetting in data management?

a.

Extracting knowledge before deleting data.

b.

Storing all data indefinitely.

c.

Deleting data without extraction.

d.

Using fixed lifetime policies.

Discuss
Answer: (a).Extracting knowledge before deleting data. Explanation:One potentially viable approach to forgetting in data management is to extract knowledge as much as possible before deleting data.
Q76.
What does the approach of "forgetting before storing" propose?

a.

Storing all incoming data.

b.

Extracting knowledge from all incoming data.

c.

Storing only data that matches the knowledge genome.

d.

Deleting data without extraction.

Discuss
Answer: (c).Storing only data that matches the knowledge genome. Explanation:The approach of "forgetting before storing" proposes storing only data that matches the knowledge genome to a sufficient extent, based on similarity with existing knowledge.
Q77.
What is data contextualization primarily concerned with in the context of the Big Data?

a.

Reorganizing data for efficient storage.

b.

Understanding the source of data.

c.

Separating predictive and contextual features of data.

d.

Encrypting data for security.

Discuss
Answer: (b).Understanding the source of data. Explanation:Data contextualization, in the context of the Big Data, is primarily concerned with understanding the source of data, including information about its origin, collection, and context of use.
Q78.
What does data contextualization involve transforming data from and into?

a.

Transforming data from raw to processed.

b.

Transforming data from structured to unstructured.

c.

Decontextualizing data from the context of use and recontextualizing it.

d.

Transforming data from old to new formats.

Discuss
Answer: (c).Decontextualizing data from the context of use and recontextualizing it. Explanation:Data contextualization involves transforming data by decontextualizing it from the context of origin and recontextualizing it into the context of use.
Q79.
What is the primary benefit of using dynamic contextualization in knowledge discovery?

a.

It simplifies the data mining process.

b.

It reduces the need for encryption.

c.

It improves the quality of results.

d.

It separates data into predictive and contextual features.

Discuss
Answer: (c).It improves the quality of results. Explanation:The primary benefit of using dynamic contextualization in knowledge discovery is that it improves the quality of results compared to "static" approaches.
Q80.
What is the primary purpose of data compression in the context of Big Data?

a.

To increase the size of data for more detailed analysis.

b.

To preserve all essential features of the data.

c.

To reduce storage space and communication overhead.

d.

To remove any repetitive data fragments.

Discuss
Answer: (c).To reduce storage space and communication overhead. Explanation:In the context of Big Data, data compression primarily aims to reduce storage space and communication overhead while preserving essential features either fully (lossless compression) or partly (lossy compression).
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