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Question 1

What technique is commonly used to handle imbalanced datasets by creating synthetic samples of the minority class?

Options :

A : SMOTE

B : Undersampling the majority class

C : Oversampling the minority class

D : Regularization

Answer: A

Question 2

Which of the following is a common issue with data characterized by a lack of sufficient data points relative to the number of features?

Options :

A : Sparse data

B : Multicollinearity

C : Non-stationarity

D : Non-linearity

Answer: A

Question 3

Which machine learning algorithm is suitable for handling imbalanced datasets?

Options :

A : Decision trees

B : Naive Bayes

C : Random forest

D : Support vector machines

Answer: C

Question 4

What is the primary purpose of the CRISP-DM (Cross-Industry Standard Process for Data Mining) framework in the context of data science projects?

Options :

A : To provide a structured approach to the entire data science life cycle

B : To optimize model performance and accuracy

C : To facilitate collaboration among team members

D : To automate data preprocessing and feature engineering

Answer: A

Question 5

What is the primary objective of hierarchical clustering in unsupervised learning?

Options :

A : To evaluate the performance of a trained model

B : To group similar data points into clusters based on their proximity

C : To preprocess the input data before training the model

D : To select the best hyperparameters for the model

Answer: B