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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 9 of 43

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Q81.
When is lossy compression typically applied to data?

a.

When data fragments are known to be repetitive.

b.

When data will be used in an unknown manner.

c.

When all essential data features must be preserved.

d.

When data clusters have quasi-periodical features.

Discuss
Answer: (b).When data will be used in an unknown manner. Explanation:Lossy compression is typically applied to data when it is known how the data will be used, at least potentially. This means that some data fractions may be sacrificed without losing the required facets of semantics and overall data quality.
Q82.
What are the four major functional areas of autonomic computing according to IBM?

a.

Self-awareness, self-configuration, self-optimization, and self-protection.

b.

Self-optimization, self-protection, self-awareness, and self-adaptation.

c.

Self-configuration, self-optimization, self-protection, and self-healing.

d.

Self-configuration, self-optimization, self-protection, and self-awareness.

Discuss
Answer: (d).Self-configuration, self-optimization, self-protection, and self-awareness. Explanation:According to IBM, the four major functional areas of autonomic computing are self-configuration, self-optimization, self-protection, and self-awareness.
Q83.
What is the primary benefit of sharing knowledge within a group?

a.

Enhancing individual performance

b.

Increasing the quality of life of individuals

c.

Improving group collaboration

d.

All of the above

Discuss
Answer: (d).All of the above Explanation:Sharing knowledge within a group has multiple benefits, including enhancing individual performance, increasing the quality of life of individuals, and improving group collaboration.
Q84.
How does knowledge evolve in a social context?

a.

Through natural selection

b.

Through explicitation

c.

Through expressiveness

d.

All of the above

Discuss
Answer: (d).All of the above Explanation:Knowledge evolves in a social context through various processes, including natural selection, explicitation, expressiveness, and morphogenesis.
Q85.
What is the role of ontologies in understanding Big Data?

a.

They serve as data repositories

b.

They represent evolving knowledge species

c.

They are used for data collection

d.

They define data standards

Discuss
Answer: (b).They represent evolving knowledge species Explanation:Ontologies play a crucial role in understanding Big Data by representing evolving knowledge species.
Q86.
Why is it important to treat Big Data processing as an ecosystem of evolving processing entities?

a.

To simplify data management

b.

To ensure data consistency

c.

To adapt to changing data cultures

d.

To reduce data complexity

Discuss
Answer: (c).To adapt to changing data cultures Explanation:Treating Big Data processing as an ecosystem of evolving processing entities is important to adapt to changing data cultures and complex processes.
Q87.
What is the primary challenge faced by traditional relational database management technologies when dealing with big data analytics?

a.

Difficulty in indexing data

b.

Slow data retrieval

c.

Inability to handle structured data

d.

Decreased data insertion speeds

Discuss
Answer: (d).Decreased data insertion speeds Explanation:Traditional relational database management technologies struggle with decreased data insertion speeds when dealing with big data analytics.
Q88.
How do companies like Facebook and Twitter achieve scalability for their MySQL installations?

a.

By using consistent hashing

b.

By adding more central processing units

c.

By implementing deep data structures

d.

By optimizing data retrieval

Discuss
Answer: (a).By using consistent hashing Explanation:Companies like Facebook and Twitter achieve scalability for their MySQL installations by using consistent hashing to shard (partition) their data across multiple database servers.
Q89.
What is the main difference between vertical scalability and horizontal scalability with database products?

a.

Vertical scalability involves adding more machines, while horizontal scalability adds more capacity to a single machine.

b.

Vertical scalability requires manual data rebalancing, while horizontal scalability is handled automatically by the database system.

c.

Vertical scalability is client-side, while horizontal scalability is server-side.

d.

Vertical scalability is suitable for linear data growth, while horizontal scalability is better for exponential data growth.

Discuss
Answer: (b).Vertical scalability requires manual data rebalancing, while horizontal scalability is handled automatically by the database system. Explanation:The main difference is that vertical scalability involves adding more capacity to a single machine, while horizontal scalability requires manual data rebalancing and is handled automatically by the database system.
Q90.
What should be considered when constructing Big Data systems on premise?

a.

Linear data growth patterns

b.

Avoidance of complex data transformations

c.

High capital investment

d.

Manual rebalancing of data

Discuss
Answer: (c).High capital investment Explanation:Constructing Big Data systems on premise requires a greater capital investment, among other considerations.
Page 9 of 43

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