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Data Science Quiz 1. Which programming language is popularly known for its libraries in data analysis and machine learning? a) Java b) Ruby c) C# d) Python Answer: d) Python 2. Which of the following is NOT a typical step in the data preprocessing phase? a) Data Cleaning b) Feature Engineering c) Data Visualization d) Data Compression Answer: d) Data Compression 3. Which method is commonly used to handle missing data? a) One-hot encoding b) Decision trees c) Imputation d) Gradient descent Answer: c) Imputation 4. Which of the following is a popular algorithm for unsupervised learning? a) Linear Regression b) Decision Tree c) K-Means Clustering d) Logistic Regression Answer: c) K-Means Clustering 5. In which scenario is overfitting likely to occur? a) When a model is too simple b) When a model is trained with a small amount of data c) When a model has a high bias d) When a model does not use any features Answer: b) When a model is trained with a small amount of data 6. What is the main purpose of a train-test split in a dataset? a) To increase the size of the dataset b) To evaluate the model's performance on unseen data c) To ensure that all features are equally distributed d) To perform feature scaling Answer: b) To evaluate the model's performance on unseen data 7. Which of the following metrics is NOT used for evaluating classification models? a) Root Mean Square Error (RMSE) b) Precision c) Recall d) F1-Score Answer: a) Root Mean Square Error (RMSE) 8. Which library in Python is specifically designed for data visualization? a) NumPy b) Pandas c) Scikit-learn d) Matplotlib Answer: d) Matplotlib 9. In a Decision Tree, what does the depth of the tree signify? a) The number of features used b) The total data points available c) The complexity of the model d) The accuracy of the model Answer: c) The complexity of the model 10. Which of the following is NOT a benefit of using ensemble methods in machine learning? a) Reducing variance b) Increasing model interpretability c) Reducing bias d) Improving accuracy Answer: b) Increasing model interpretability