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Question 1

What technique is commonly used for imputing missing values in a dataset by replacing them with randomly selected values from the variable?

Options :

A : Regression imputation

B : Random imputation

C : Mean imputation

D : Mode imputation

Answer: B

Question 2

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 3

A data analyst wants to find the latitude and longitude of a mailing address. Which of the following is the best method to use?

Options :

A : One-hot encoding

B : Binning

C : Geocoding

D : Imputing

Answer: C

Question 4

What is the primary objective of feature importance analysis in machine learning?

Options :

A : To select the best hyperparameters for the model

B : To preprocess the input data before training the model

C : To identify the most influential features for predicting the target variable

D : To evaluate the performance of a trained model

Answer: C

Question 5

Which of the following is an essential step in the data science life cycle that involves validating the accuracy and effectiveness of a trained model using unseen data?

Options :

A : Model tuning

B : Model evaluation

C : Model selection

D : Model deployment

Answer: B