1. Which of the following best describes the first step in a data science project?
2. What is the purpose of a Proof of Concept (POC) in a data science project?
3. Which of the following scenarios would best classify as a regression problem in data science?
4. What is the most effective way to avoid scope creep in a data science project?
5. Which of the following data privacy regulations mandates strict guidelines for handling personal data in the European Union?
6. Which of the following best defines a minimal viable product (MVP) in the context of data science?
7. What is a common challenge when identifying stakeholder requirements for a data science project?
8. Which of the following methods is most commonly used to remove duplicates from a dataset in Python?
9. When loading a dataset from a CSV file into a pandas DataFrame in Python, which of the following commands is used?
10. Which of the following describes an effective way to handle missing data in a dataset?
11. What is the most important consideration when extracting data from multiple sources for analysis?
12. Which Python library is commonly used to interact with SQL databases to extract data for analysis?
13. When merging two datasets in Python using pandas, which method should be used to perform a left join?
14. In the context of data extraction, which of the following is the primary role of an API (Application Programming Interface)?
15. Which of the following techniques is most suitable for handling missing values in categorical data?
16. What is the main advantage of using cloud storage solutions like AWS S3 or Azure Data Lake for data extraction and loading?
17. Which of the following commands can be used to load data from an SQL database into a pandas DataFrame in Python?
18. Which of the following techniques is used to handle imbalanced datasets in machine learning?
19. In the context of data transformation, what is one purpose of feature scaling?
20. Which Python library is primarily used for scraping web data to be used in data science projects?
21. When dealing with a dataset that has a significant number of outliers, which of the following is an effective way to mitigate their impact?
22. What is the primary goal of exploratory data analysis (EDA)?
23. Which of the following is used to visualize the distribution of a single numerical variable in a dataset?
24. Which of the following measures is used to detect the central tendency of a dataset?
25. Which of the following is an effective way to detect outliers in a dataset?
26. What is the purpose of feature engineering in exploratory data analysis?
27. Which of the following methods is used to assess correlations between numerical features in a dataset?
28. In Python, which pandas function can be used to generate summary statistics for a DataFrame?
29. What is the effect of normalizing data during EDA?
30. When exploring a dataset, what is the purpose of generating a scatter plot?
31. Which of the following indicates a strong positive linear relationship between two variables?
32. What is the purpose of using dimensionality reduction techniques like Principal Component Analysis (PCA) during EDA?
33. In exploratory data analysis, which plot would be most appropriate to visualize the distribution of outliers?
34. Which of the following preprocessing steps is recommended before performing EDA on a dataset?
35. Which of the following techniques is used to scale data such that the mean is zero and the standard deviation is one?
36. In Python, which library is commonly used for creating data visualizations such as histograms, scatter plots, and bar charts?
37. Which method is used in Python to calculate the correlation matrix of a pandas DataFrame?
38. In a correlation matrix, what does a value close to 0 indicate?
39. When analyzing categorical data, which visualization is typically used to display the frequency distribution of different categories?
40. Which of the following is an example of feature engineering in EDA?
41. Which statistical measure is most sensitive to outliers in a dataset?
42. Which of the following tasks should be performed before training a machine learning model?
43. Which technique is commonly used to tune hyperparameters in a machine learning model?
44. You have trained a logistic regression model and obtained the following confusion matrix for a binary classification problem:
45. Which machine learning model is most suitable for predicting a continuous numerical variable, such as house prices?
46. Which of the following techniques is effective in preventing overfitting in machine learning models?
47. A model is trained to predict whether a patient has a disease. You are given the following confusion matrix: Actual Positive Actual Negative Predicted Positive 70 Predicted Negative 10 Calculate the precision of the model for the positive class.
48. Which of the following is an appropriate evaluation metric for a regression model?
49. What is the purpose of using the train-test split method in machine learning?
50. Which machine learning algorithm is suitable for solving a binary classification problem?
51. Which of the following is used to compare the performance of different machine learning models on a dataset?
52. Which algorithm is most commonly used for clustering data points into groups based on their similarities?
53. Which of the following algorithms is most suitable for a multiclass classification problem?
54. Which metric is used to evaluate a model's ability to generalize to unseen data?
55. A regression model has a Mean Squared Error (MSE) of 25. What is the Root Mean Squared Error (RMSE) for this model?
56. Which of the following methods is used to handle imbalanced datasets during model training?
57. A model predicts the sales of a product and gives the following results for actual and predicted values:
58. Which type of regularization penalizes the sum of the absolute values of the coefficients in a model?
59. Which of the following techniques reduces the dimensionality of data while preserving variance?
60. What is the key benefit of using ensemble methods like Random Forest in model building?
61. Which of the following is a key reason to use a test set after training a machine learning model?
62. You are given a regression model with the following predictions and actual values for a test set:
63. Which of the following metrics is commonly used to evaluate the performance of a classification model on the test set?
64. A binary classifier is tested on a dataset, and the following confusion matrix is generated:
65. Why is cross-validation an important step when testing machine learning models?
66. What is the primary goal of model operationalization in data science?
67. Which of the following is a common way to deploy a machine learning model in production environments?
68. Which of the following best describes model monitoring in a production environment?
69. What is the primary purpose of versioning machine learning models during deployment?
70. What is a key challenge when operationalizing machine learning models in production?
71. Which of the following tools is commonly used to deploy machine learning models in a cloud environment?
72. Which of the following is the most important factor when presenting data science results to non-technical stakeholders?
73. Which type of visualization is most effective for showing the relationship between two numerical variables?
74. When presenting model performance to a business audience, which of the following metrics is typically the most important?
75. Which of the following is a key principle of data storytelling when communicating results?
76. Given the following confusion matrix, calculate the accuracy of the model:
77. Based on the confusion matrix provided below, calculate the precision of the model for the positive class:
78. For the following confusion matrix, calculate the recall for the positive class:
79. Using the following confusion matrix, calculate the F1 score for the positive class:
80. A binary classification model has the following confusion matrix:
81. You are given the following confusion matrix:
82. A model predicts a total of 100 samples, with the following breakdown:
83. You are given the following confusion matrix for a binary classifier:
84. In a binary classification task, a model gives the following predicted probabilities for class 1:
85. In a model performance evaluation, what does the ROC-AUC score represent?
86. A model's predicted class probabilities for a binary classification task are as follows:
87. Which of the following statements best describes sensitivity (recall) in a binary classification model?
88. In a machine learning pipeline, why is it important to evaluate a model on unseen test data?
89. In the context of model performance, what does the precision-recall curve highlight?
90. Which of the following is most useful for assessing the performance of a model on imbalanced datasets?