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Welcome to the Advanced Data Analytics for Business MCQs Page

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

Advanced Data Analytics for Business MCQs | Page 11 of 15

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Q101.
What is one of the improved versions of the ID3 decision tree algorithm?

a.

C4.5

b.

ID4

c.

C3.0

d.

D5.0

Discuss
Answer: (a).C4.5 Explanation:C4.5 is an improved version of the ID3 decision tree algorithm.
Q102.
What does the "rxDTree" function in Revolution R* Enterprise 6.1 software provide for decision tree learning?

a.

Vertical data parallelism

b.

Noisy data handling

c.

Horizontal data parallelism

d.

Clustering algorithms

Discuss
Answer: (c).Horizontal data parallelism Explanation:The "rxDTree" function in Revolution R* Enterprise 6.1 software provides horizontal data parallelism for decision tree learning.
Q103.
How does data parallelism work in the context of Big Data decision trees?

a.

It builds different tree nodes on different processors.

b.

It partitions data horizontally, so different processors see different observations.

c.

It focuses on handling missing values in the dataset.

d.

It converts decision trees into sets of if-then rules.

Discuss
Answer: (b).It partitions data horizontally, so different processors see different observations. Explanation:Data parallelism in Big Data decision trees partitions data horizontally so that different processors see different observations.
Q104.
What is the potential drawback of using the "rxDTree" function for decision tree construction?

a.

It may result in a suboptimal split point being chosen.

b.

It is not designed for Big Data applications.

c.

It requires a classical algorithm for preprocessing.

d.

It is not suitable for classification tasks.

Discuss
Answer: (a).It may result in a suboptimal split point being chosen. Explanation:The "rxDTree" function may choose a suboptimal split point since it examines only a limited number of split locations.
Q105.
What are decision trees converted into to improve readability?

a.

Histograms

b.

Clusters

c.

Sets of if-then rules

d.

Graphs

Discuss
Answer: (c).Sets of if-then rules Explanation:Decision trees can be converted into sets of if-then rules to improve readability and interpretability.
Q106.
What is one way to obtain a more readable representation of a decision tree?

a.

Convert it into a set of rules

b.

Convert it into a neural network

c.

Convert it into a regression model

d.

Convert it into a graph

Discuss
Answer: (a).Convert it into a set of rules Explanation:Decision trees can be converted into sets of if-then rules to improve readability.
Q107.
What is the covering approach in rule induction?

a.

It seeks ways to cover all instances in each class simultaneously.

b.

It seeks ways to exclude instances from all classes.

c.

It focuses on clustering data.

d.

It converts decision trees into decision forests.

Discuss
Answer: (a).It seeks ways to cover all instances in each class simultaneously. Explanation:The covering approach in rule induction identifies a rule that "covers" some of the instances in each class while excluding instances not in the class.
Q108.
What is the main difference between clustering and classification?

a.

Clustering is unsupervised, while classification is supervised.

b.

Clustering is always based on predefined groups, while classification is not.

c.

Clustering and classification are the same concepts with different names.

d.

Clustering involves transforming data into decision trees.

Discuss
Answer: (a).Clustering is unsupervised, while classification is supervised. Explanation:Clustering is unsupervised, meaning that groups are not predefined, while classification is supervised.
Q109.
How are clusters defined in clustering?

a.

As sets of dissimilar elements

b.

As sets of alike elements

c.

As predefined groups

d.

As decision trees

Discuss
Answer: (b).As sets of alike elements Explanation:Clusters in clustering are defined as sets of alike elements.
Q110.
Which metric is commonly used to calculate distances in the k-means clustering algorithm?

a.

Jaccard similarity

b.

Hamming distance

c.

Euclidean distance

d.

Cosine similarity

Discuss
Answer: (c).Euclidean distance Explanation:The k-means clustering algorithm commonly uses Euclidean distance to calculate distances between data points.
Page 11 of 15

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