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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 28 of 43

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Q271.
Why is it important to define rules for arranging and filtering data in Big Data solutions?

a.

To reduce data size

b.

To improve network performance

c.

To avoid inconsistencies and reduce resource consumption

d.

To speed up data processing

Discuss
Answer: (c).To avoid inconsistencies and reduce resource consumption Explanation:Defining rules for arranging and filtering data is important in Big Data solutions to avoid inconsistencies and reduce resource consumption when connection is lost and storage is partitioned.
Q272.
What is the challenge related to data rendering when dealing with huge data sets?

a.

Data rendering is not necessary for huge data sets.

b.

Data rendering can be highly computational-intensive.

c.

Data rendering is only relevant for small data sets.

d.

Data rendering is limited to specific data types.

Discuss
Answer: (b).Data rendering can be highly computational-intensive. Explanation:The challenge related to data rendering with huge data sets is that it can be highly computational-intensive.
Q273.
How can efficient access to Big Data results be guaranteed?

a.

By using standard interfaces for data access

b.

By reducing the size of the data results

c.

By limiting data availability

d.

By avoiding data rendering

Discuss
Answer: (a).By using standard interfaces for data access Explanation:Efficient access to Big Data results can be guaranteed by using standard interfaces for data access.
Q274.
What is a common feature of faceted results in data access?

a.

They do not allow users to further restrict search results.

b.

They are not suitable for business and medical applications.

c.

They do not use semantic queries.

d.

They allow users to further restrict search results using "and"/"or" filters.

Discuss
Answer: (d).They allow users to further restrict search results using "and"/"or" filters. Explanation:Faceted results in data access allow users to further restrict search results using "and"/"or" filters.
Q275.
How does the number of nodes in a cluster affect job completion times in Big Data solutions?

a.

It has no impact on job completion times.

b.

A greater number of nodes in a cluster corresponds to longer job completion times.

c.

A greater number of nodes in a cluster corresponds to shorter job completion times.

d.

Job completion times are not related to the number of nodes in a cluster.

Discuss
Answer: (c).A greater number of nodes in a cluster corresponds to shorter job completion times. Explanation:A greater number of nodes in a cluster corresponds to shorter job completion times in Big Data solutions.
Q276.
What is the role of memory caching or memory-based storage in high-performance databases?

a.

To slow down data access times

b.

To reduce the need for CPU processing

c.

To optimize memory performance

d.

To improve disk performance

Discuss
Answer: (c).To optimize memory performance Explanation:Memory caching or memory-based storage is used in high-performance databases to optimize memory performance.
Q277.
How does network capability impact the performance of Big Data management?

a.

It has no impact on performance.

b.

It only affects write operations.

c.

It affects both read and write operations.

d.

It only affects read operations.

Discuss
Answer: (c).It affects both read and write operations. Explanation:Network capability impacts both read and write operations in Big Data management.
Q278.
What is the CAP theorem and how does it relate to Big Data solutions?

a.

The CAP theorem states that Big Data solutions must provide three main features: consistency, availability, and partition tolerance.

b.

The CAP theorem states that any distributed storage system for sharing data can provide only two of the three main features: consistency, availability, and partition tolerance.

c.

The CAP theorem states that Big Data solutions are not subject to consistency, availability, or partition tolerance.

d.

The CAP theorem states that Big Data solutions must prioritize consistency over availability and partition tolerance.

Discuss
Answer: (b).The CAP theorem states that any distributed storage system for sharing data can provide only two of the three main features: consistency, availability, and partition tolerance. Explanation:The CAP theorem states that any distributed storage system for sharing data can provide only two of the three main features: consistency, availability, and partition tolerance, and it relates to the trade-offs made in Big Data solutions.
Q279.
How do NoSQL databases differ from traditional relational databases?

a.

NoSQL databases are less scalable than relational databases.

b.

NoSQL databases are not suitable for Big Data.

c.

NoSQL databases do not support transactions.

d.

NoSQL databases use different data models and are often more scalable.

Discuss
Answer: (d).NoSQL databases use different data models and are often more scalable. Explanation:NoSQL databases differ from traditional relational databases in that they use different data models and are often more scalable, making them suitable for Big Data solutions.
Q280.
What is the BASE property in NoSQL databases?

a.

Basic Available, Strong state, and Eventual consistent

b.

Basic Accessible, Stable state, and Eventual consistent

c.

Basic Available, Soft state, and Eventual consistent

d.

Basic Accessible, Strong state, and Eventual consistent

Discuss
Answer: (c).Basic Available, Soft state, and Eventual consistent Explanation:The BASE property in NoSQL databases stands for Basic Available, Soft state, and Eventual consistent.
Page 28 of 43

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