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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.

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Big Textual Data Analytics and Knowledge Management MCQs | Page 2 of 11

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Q11.
What is the primary issue with analytics based on textual data?

a.

Lack of context in text analysis

b.

Lack of advanced language models

c.

High counts of words in large vocabularies

d.

Parameter smoothing given limited evidence

Discuss
Answer: (d).Parameter smoothing given limited evidence Explanation:The primary issue with analytics based on textual data is parameter smoothing given limited evidence.
Q12.
What is the key problem with unstructured data?

a.

Lack of schema for data types

b.

High-dimensional variable identification

c.

Lack of relevant information

d.

The need for advanced language models

Discuss
Answer: (b).High-dimensional variable identification Explanation:The key problem with unstructured data is the challenge of high-dimensional variable identification.
Q13.
How is the problem of variable identification related to the analysis of textual data?

a.

It affects the selection of data types.

b.

It influences context-based analysis.

c.

It is primarily related to image preprocessing.

d.

It results from a lack of language models.

Discuss
Answer: (b).It influences context-based analysis. Explanation:The problem of variable identification is related to the analysis of textual data in the sense that it influences context-based analysis.
Q14.
Why is parameter smoothing used in analytics based on textual data?

a.

To increase word sequence probabilities

b.

To eliminate the need for large corpora

c.

To reduce the number of possible multiwords

d.

To address context-based challenges

Discuss
Answer: (a).To increase word sequence probabilities Explanation:Parameter smoothing is used in analytics based on textual data to increase word sequence probabilities.
Q15.
What is the main issue with the size of vocabularies in the context of textual data analytics?

a.

The lack of stemming components

b.

The need for advanced schema

c.

The difficulty in smoothing frequency estimates

d.

The challenge of high-dimensional variable identification

Discuss
Answer: (c).The difficulty in smoothing frequency estimates Explanation:The main issue with the size of vocabularies in textual data analytics is the difficulty in smoothing frequency estimates.
Q16.
In image processing, what is the purpose of image segmentation?

a.

Recognizing relationships between objects

b.

Identifying typical scenes in images

c.

Decomposing an image into parts

d.

Recognizing specific objects

Discuss
Answer: (c).Decomposing an image into parts Explanation:In image processing, the purpose of image segmentation is to decompose an image into parts.
Q17.
What is the key purpose of object recognition in image processing?

a.

Identifying typical object configurations

b.

Recognizing spatial relationships between objects

c.

Recognizing relationships with intentional information

d.

Detecting novelties in images

Discuss
Answer: (a).Identifying typical object configurations Explanation:The key purpose of object recognition in image processing is identifying typical object configurations.
Q18.
In unstructured information processing, why is segmentation considered a prerequisite for further processing steps?

a.

To detect novel objects

b.

To recognize named entities

c.

To identify typical object configurations

d.

To facilitate subsequent processing

Discuss
Answer: (d).To facilitate subsequent processing Explanation:In unstructured information processing, segmentation is considered a prerequisite for further processing steps to facilitate subsequent processing.
Q19.
What is the practical importance of novelty detection in unstructured information processing?

a.

Detecting typical object configurations

b.

Recognizing spatial relationships

c.

Finding objects unrelated to the theme or scene

d.

Identifying named entities

Discuss
Answer: (c).Finding objects unrelated to the theme or scene Explanation:The practical importance of novelty detection in unstructured information processing is in finding objects unrelated to the theme or scene.
Q20.
What can object relationships include in image processing?

a.

Grammatical forms

b.

Intentional information

c.

Clustering results

d.

Typical scenes

Discuss
Answer: (b).Intentional information Explanation:Object relationships in image processing can include intentional information.
Page 2 of 11

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