questions.txt

Question 1
Which of the following is an example of Machine Learning?

1 point

Streaming service viewing suggestions.


Websites recommending items to purchase.


Telecommunication companies predicting subscriber retention.


All of the above.

2.
Question 2
Which of the following is a Machine Learning technique?

1 point

Clustering


Classification


Regression/Estimation


Associations


All of the above

3.
Question 3
Multiple Linear Regression is appropriate for:

1 point

Predicting tomorrow's rainfall amount based on the wind speed and temperature


Predicting the sales amount based on month


Predicting whether a drug is effective for a patient based on her characteristics

4.
Question 4
Which of the following statements are TRUE about Polynomial Regression?

1 point

Polynomial regression can use the same mechanism as Multiple Linear Regression to find the parameters.


Polynomial regression models can fit using the Least Squares method.


Polynomial regression fits a curve line to your data.

5.
Question 5
Which of the below is a sample of classification problem?

1 point

To predict the category to which a customer belongs to.


To predict whether a customer switches to another provider/brand. 


To predict whether a customer responds to a particular advertising campaign or not.


All of the above

6.
Question 6
Which of the following is FALSE for Logistic Regression?

1 point

Logistic regression can be used for both binary classification and multi-class classification.


Logistic regression models the relationship between two variables by fitting a linear equation to observe data, using an explanatory variable and a dependent variable.


In logistic regression, the dependent variable is binary.


Logistic regression is analogous to linear regression but takes a categorical/discrete target field instead of a numeric one.

7.
Question 7
Which of the following statements is true for k-means clustering?

1 point

k-means divides the data into non-overlapping clusters without any cluster-interval structure.


The object of k-means is to form clusters in such a way that similar samples go into a cluster, and dissimilar samples fall into different clusters.


Is one of the simplest unsupervised learning algorithms that solve well known clustering problems.


 *D: All of the above.

8.
Question 8
What are the two parameters for DBSCAN?

1 point

Clusters and Minimum Points


Epsilon and Maximum Points


Clusters and Epsilon


Epsilon and Minimum Points

9.
Question 9
A _______________ system provides a better experience for the user by giving them a broader exposure to many different products they might be interested in.

1 point

Resource


Relationship


Recommender


Reinforcement

10.
Question 10
Which of the following is NOT true regarding content-based recommendation systems?

1 point

Content-based recommendation system tries to recommend items based on similarity among items.


Content-based recommendation system tries to recommend items based on the similarity of users when buying, watching, or enjoying something.


Content-based recommendation system tries to recommend items based on the preferences of people living in your area.


All of the above.