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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 10 of 15

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Q91.
In supervised learning, how are the classes determined?

a.

By using unsupervised clustering algorithms

b.

Before examining the data

c.

Based on the frequency of occurrence in the data

d.

By applying a similarity metric

Discuss
Answer: (b).Before examining the data Explanation:In supervised learning, the classes are determined before examining the data, and the algorithm learns to map examples into these predefined classes.
Q92.
What is one of the basic tasks in supervised learning?

a.

Clustering

b.

Dimensionality reduction

c.

Classification

d.

Association rule mining

Discuss
Answer: (c).Classification Explanation:One of the basic tasks in supervised learning is classification, where data instances are mapped into predefined classes.
Q93.
What distinguishes unsupervised learning from supervised learning?

a.

Unsupervised learning does not use machine learning algorithms.

b.

Unsupervised learning does not require training data.

c.

Unsupervised learning does not involve data grouping.

d.

Unsupervised learning does not have predefined classes.

Discuss
Answer: (d).Unsupervised learning does not have predefined classes. Explanation:Unsupervised learning differs from supervised learning in that it does not have predefined classes or labels for the data.
Q94.
What is a common goal of unsupervised learning?

a.

Dimensionality reduction

b.

Classification

c.

Clustering

d.

Regression analysis

Discuss
Answer: (c).Clustering Explanation:A common goal of unsupervised learning is clustering, where data instances are grouped together based on similarity.
Q95.
Why is machine learning well-suited for handling Big Data?

a.

Because Big Data is primarily structured and organized

b.

Because machine learning algorithms are simple and efficient

c.

Because Big Data comes from sources of various types and complexity

d.

Because machine learning algorithms cannot handle complex data

Discuss
Answer: (c).Because Big Data comes from sources of various types and complexity Explanation:Machine learning is well-suited for handling Big Data because Big Data comes from sources of various types and complexity, and machine learning can extract knowledge and meaning from such data.
Q96.
What types of tasks are strengths of machine learning algorithms in the context of Big Data?

a.

Predictive and descriptive tasks

b.

Rote learning and memorization

c.

Structured data analysis only

d.

Human-driven data exploration

Discuss
Answer: (a).Predictive and descriptive tasks Explanation:Machine learning algorithms are strong at performing predictive and descriptive tasks, which are valuable for extracting knowledge and meaning from Big Data.
Q97.
What is the primary purpose of decision tree learning?

a.

Regression analysis

b.

Classification

c.

Clustering

d.

Dimensionality reduction

Discuss
Answer: (b).Classification Explanation:Decision tree learning is primarily used for classification tasks.
Q98.
How are decision tree algorithms like ID3 and C4.5 used in the learning process?

a.

They select attributes based on the order they appear in the dataset.

b.

They use a subset of instances to construct the initial decision tree.

c.

They convert decision trees into sets of if-then rules.

d.

They focus on dealing with numeric attributes and missing values.

Discuss
Answer: (b).They use a subset of instances to construct the initial decision tree. Explanation:Decision tree algorithms use a subset of instances from the training set to construct the initial decision tree.
Q99.
What measures are used to determine the selection of attributes for splitting in decision trees?

a.

Information gain or gain ratio

b.

Mean and standard deviation

c.

Principal component analysis

d.

F-statistic

Discuss
Answer: (a).Information gain or gain ratio Explanation:Decision tree algorithms use measures like information gain or gain ratio to select attributes for splitting.
Q100.
What happens when an instance is incorrectly classified in the decision tree learning process?

a.

The instance is ignored, and the tree is built from the remaining instances.

b.

The tree is rebuilt from scratch.

c.

The instance is added to the selected subset of training instances, and a new tree is constructed.

d.

The decision tree is pruned to remove unnecessary branches.

Discuss
Answer: (c).The instance is added to the selected subset of training instances, and a new tree is constructed. Explanation:When an instance is incorrectly classified, it is added to the selected subset of training instances, and a new tree is constructed.
Page 10 of 15

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