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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 12 of 15

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Q111.
What is the challenge associated with the k-means algorithm?

a.

Sensitivity to initial centroid placements

b.

Difficulty in finding a large number of clusters

c.

Inability to handle high-dimensional data

d.

Lack of scalability

Discuss
Answer: (a).Sensitivity to initial centroid placements Explanation:The k-means algorithm is sensitive to the initial placements of centroids, and it can easily settle into local minima.
Q112.
How can the challenge associated with the k-means algorithm be mitigated?

a.

By increasing the number of clusters (k)

b.

By reducing the dimensionality of the data

c.

By running the algorithm multiple times with different initializations

d.

By using hierarchical clustering instead

Discuss
Answer: (c).By running the algorithm multiple times with different initializations Explanation:To mitigate the challenge of sensitivity to initial centroid placements, it is common to run the k-means algorithm multiple times with different seeds and choose the results with the least total squared distance.
Q113.
Which category of clustering algorithms creates a nested set of clusters?

a.

Partitional

b.

Agglomerative

c.

Divisive

d.

K-means

Discuss
Answer: (b).Agglomerative Explanation:Hierarchical clustering algorithms create a nested set of clusters, and agglomerative clustering is a type of hierarchical clustering.
Q114.
What is the primary difference between agglomerative and divisive hierarchical clustering?

a.

Agglomerative works in a bottom-up fashion, while divisive works in a top-down fashion.

b.

Agglomerative uses partitional clustering, while divisive uses hierarchical clustering.

c.

Agglomerative requires a fixed number of clusters, while divisive does not.

d.

Agglomerative uses Euclidean distance, while divisive uses Manhattan distance.

Discuss
Answer: (a).Agglomerative works in a bottom-up fashion, while divisive works in a top-down fashion. Explanation:Agglomerative hierarchical clustering works in a bottom-up fashion, while divisive hierarchical clustering works in a top-down fashion.
Q115.
In logistic regression, what is the dependent variable typically referred to as?

a.

Feature

b.

Hypothesis

c.

Independent variable

d.

Dichotomy

Discuss
Answer: (d).Dichotomy Explanation:In logistic regression, the dependent variable is typically referred to as the dichotomy, representing two categories.
Q116.
What does the conditional probability of a category given the input represent in logistic regression?

a.

The probability of a category occurring

b.

The correlation between features

c.

The number of categories

d.

The weight of the independent variable

Discuss
Answer: (a).The probability of a category occurring Explanation:The conditional probability of a category given the input represents the probability of that category occurring based on the input features.
Q117.
How is logistic regression different from linear regression?

a.

Logistic regression assumes a linear relationship between variables.

b.

Logistic regression requires normally distributed variables.

c.

Logistic regression does not assume linearity between variables.

d.

Logistic regression assumes homoscedasticity.

Discuss
Answer: (c).Logistic regression does not assume linearity between variables. Explanation:Logistic regression does not assume linearity between independent variables and the dependent variable, unlike linear regression.
Q118.
What type of table shows correct and incorrect classifications in logistic regression?

a.

Classification table

b.

Linear regression table

c.

Homoscedasticity table

d.

Logistic table

Discuss
Answer: (a).Classification table Explanation:In logistic regression, a classification table is used to show correct and incorrect classifications of the dependent variable.
Q119.
What is the primary focus of reinforcement learning in machine learning?

a.

Maximizing cumulative reward

b.

Minimizing cumulative reward

c.

Identifying stable control systems

d.

Modeling nonlinear dynamics

Discuss
Answer: (a).Maximizing cumulative reward Explanation:Reinforcement learning focuses on agents taking actions to maximize cumulative reward.
Q120.
Which area of study in a control system deals with understanding the effect of inputs on the output?

a.

Stability

b.

System identification

c.

Optimal control

d.

Delay analysis

Discuss
Answer: (b).System identification Explanation:System identification is the study of understanding the effect of inputs on the output in a control system.
Page 12 of 15

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