Data Preparation, Feature Engineering, Deploying ML models

Question 1

Which of the following is NOT a technique used in feature engineering?

Accepted Answer

Removing random rows from the dataset

Question 2

Oversampling can be performed by duplicating existing minority class instances or generating synthetic samples.

Accepted Answer

True

Question 3

What is the best approach if a column has too many missing values?

Accepted Answer

Drop the column

Question 4

Why is handling imbalanced datasets important in machine learning?

Accepted Answer

Because classifiers may be biased toward the majority class

Question 5

Missing values can lead to errors in data analysis and machine learning models.

Accepted Answer

True

Question 6

What type of applications can be easily built using Streamlit?

Accepted Answer

Interactive dashboards and machine learning model demos

Question 7

What is the primary goal of feature engineering?

Accepted Answer

To create and modify features to improve model performance

Question 8

What is Gradio primarily used for?

Accepted Answer

Building interactive user interfaces for machine learning models

Question 9

Data visualization is essential in exploratory data analysis to understand patterns and distributions in the dataset.

Accepted Answer

True

Question 10

Which of the following is a key step in data cleaning and preparation?

Accepted Answer

Removing duplicate data

Question 11

Streamlit allows users to create interactive web apps without requiring front-end development skills.

Accepted Answer

True

Question 12

Which of the following statements correctly defines undersampling?

Accepted Answer

Removing instances from the majority class to balance the dataset

Question 13

Which of the following is the first step in the Data Science Lifecycle?

Accepted Answer

Define and Understand the Problem

Question 14

Which of the following is NOT a key benefit of using Gradio?

Accepted Answer

Allows easy creation of web-based demos, Requires extensive front-end development experience

Question 15

What is the main purpose of exploratory data analysis (EDA)?

Accepted Answer

Identify patterns, relationships, and insights in data

Question 16

How can outliers negatively impact a machine learning model?

Accepted Answer

They cause the model to focus on extreme values

Question 17

Which of the following is NOT a part of the Data Science Lifecycle?

Accepted Answer

Writing software documentation

Question 18

What should be done if the target column contains missing values?

Accepted Answer

Drop rows with missing target values

Question 19

What is the main disadvantage of undersampling?

Accepted Answer

It removes potentially useful data

Question 20

Feature engineering helps simplify problems and improves model efficiency.

Accepted Answer

True

Question 21

Which of the following methods is most appropriate for handling a small number of missing values in a numerical dataset?

Accepted Answer

Filling missing values with the mean or median

Questions 21