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Welcome to the Big Textual Data Analytics and Knowledge Management MCQs Page

Dive deep into the fascinating world of Big Textual Data Analytics and Knowledge Management with our comprehensive set of Multiple-Choice Questions (MCQs). This page is dedicated to exploring the fundamental concepts and intricacies of Big Textual Data Analytics and Knowledge Management, a crucial aspect of Big Data Computing. In this section, you will encounter a diverse range of MCQs that cover various aspects of Big Textual Data Analytics and Knowledge Management, 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 Big Textual Data Analytics and Knowledge Management. 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 Big Textual Data Analytics and Knowledge Management. You can click on an option to test your knowledge before viewing the solution for a MCQ. Happy learning!

Big Textual Data Analytics and Knowledge Management MCQs | Page 1 of 11

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Q1.
What are the four key dimensions characterizing "Big Data"?

a.

Volume, velocity, variety/variability, and veracity

b.

Volume, velocity, validity, and verifiability

c.

Viscosity, vitality, variability, and velocity

d.

Vastness, variability, velocity, and vagueness

Discuss
Answer: (a).Volume, velocity, variety/variability, and veracity Explanation:The four key dimensions of "Big Data" are volume, velocity, variety/variability, and veracity.
Q2.
What causes variability in textual data?

a.

Multilingualism and specific vocabularies

b.

Volume and velocity

c.

Reliability and veracity

d.

Variety of text types

Discuss
Answer: (a).Multilingualism and specific vocabularies Explanation:Variability in textual data comes from multilinguality and specific vocabularies.
Q3.
What is the key problem with the content of textual data?

a.

Volume

b.

Velocity

c.

Veracity

d.

Variety

Discuss
Answer: (c).Veracity Explanation:The key problem with the content of textual data is its veracity, which relates to its reliability and trustworthiness.
Q4.
What is one of the interesting categories of content visualizations, resulting from the combination of velocity and variety?

a.

Dynamic topic landscapes

b.

ISO topic map standard

c.

Document-based trend analysis

d.

Semantic modeling

Discuss
Answer: (a).Dynamic topic landscapes Explanation:Combining velocity and variety leads to dynamically created topic landscapes.
Q5.
What is the key task of Big Data analytics regarding veracity?

a.

Detecting the variety of textual data

b.

Discerning reliable from unreliable sources

c.

Analyzing the velocity of data change

d.

Handling the volume of textual data

Discuss
Answer: (b).Discerning reliable from unreliable sources Explanation:A key task of Big Data analytics regarding veracity is to discern reliable from unreliable sources and even to automatically detect fraudulent or manipulating content.
Q6.
What is one of the reasons for the high awareness of Big Data in the industry?

a.

The challenging aspect of database and data management technologies

b.

The use of sentiment analysis tools

c.

The development of open-source components

d.

The business challenges it poses

Discuss
Answer: (d).The business challenges it poses Explanation:One of the reasons for the high awareness of Big Data in the industry is the business challenges it poses.
Q7.
What is the main problem associated with unstructured data?

a.

Lack of relevant data types

b.

The need for advanced schema

c.

High-dimensional variable identification

d.

Smoothing frequency estimates

Discuss
Answer: (c).High-dimensional variable identification Explanation:The main problem associated with unstructured data is the challenge of variable identification in high-dimensional spaces.
Q8.
What is meant by "variety" in the context of unstructured information processing and Big Data?

a.

The diversity of data types

b.

The volume of data

c.

The velocity of data change

d.

The veracity of data

Discuss
Answer: (a).The diversity of data types Explanation:In the context of unstructured information processing and Big Data, "variety" refers to the diversity of data types.
Q9.
What is the role of a stemming component?

a.

To identify word stems in multilingual texts

b.

To increase the size of the vocabulary

c.

To create complex word forms

d.

To simplify word identification

Discuss
Answer: (a).To identify word stems in multilingual texts Explanation:The role of a stemming component is to identify word stems in order to economize on the size of the vocabulary.
Q10.
What problem is associated with sparse data matrices in textual data analytics?

a.

Limited data variety

b.

Lack of historical change in multiwords

c.

Smoothing frequency estimates

d.

Difficulty in finding relevant words

Discuss
Answer: (c).Smoothing frequency estimates Explanation:The problem associated with sparse data matrices in textual data analytics is the difficulty in smoothing frequency estimates.
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