Data Preprocessing for Deep Learning QUIZ (MCQ QUESTIONS AND ANSWERS)

Question: 1

When training a machine learning model, what is the typical objective in regression tasks?

Question: 2

What is the purpose of the activation function in a neural network?

Question: 3

Which algorithm is commonly used for supervised classification tasks in machine learning, known for its simplicity and interpretability?

Question: 4

In the context of machine learning, what does the term "overfitting" refer to?

Question: 5

What is the primary goal of feature engineering in machine learning?

Question: 6

What is the primary purpose of data preprocessing in deep learning?

Question: 7

Which statistical method can be used for feature selection during data preprocessing?

Question: 8

What is the potential drawback of oversampling the minority class in a dataset?

Question: 9

Why is it essential to check for and handle imbalanced classes in classification tasks during data preprocessing?

Question: 10

What is the primary purpose of encoding categorical variables during data preprocessing?

Question: 11

How can you address the issue of multicollinearity in your features during data preprocessing?

Question: 12

Which of the following is NOT mentioned as a benefit of data preprocessing?

Question: 13

What is the purpose of cross-validation in model training and evaluation?

Question: 14

What is a common data augmentation technique for image data?

Question: 15

Which technique is NOT mentioned as a way to address imbalanced data in classification problems?

Question: 16

How can you handle timestamp or time-series data during data preprocessing?

Question: 17

What role does data augmentation play in deep learning, particularly in computer vision tasks?

Question: 18

Which of the following methods can be used for handling missing data during data preprocessing?

Question: 19

When is data whitening often used during data preprocessing in deep learning?

Question: 20

What is the primary purpose of feature scaling in deep learning?

Question: 21

When is data encoding commonly applied during data preprocessing for deep learning?

Question: 22

Why is it essential to handle class imbalance in classification tasks during data preprocessing?

Question: 23

When is dimensionality reduction applied during data preprocessing in deep learning?

Question: 24

In natural language processing (NLP), what is the purpose of text tokenization during data preprocessing?

Question: 25

Which of the following techniques can be used to address class imbalance in a classification task?

Question: 26

How does data shuffling before training a deep learning model benefit the training process?

Question: 27

Which of the following techniques can help identify and handle outliers in your data?

Question: 28

Why is one-hot encoding commonly used for handling categorical data in deep learning?

Question: 29

What is the primary objective of exploratory data analysis (EDA) during data preprocessing?

Question: 30

What is the purpose of data normalization in deep learning?