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Data Engineering Foundations
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Quiz Part 3: Data Cleaning and Preprocessing
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Quiz Part 3: Data Cleaning and Preprocessing

Answers

  1. b) Variance Thresholding
  2. c) Reindexing the data to a regular frequency and using forward-fill or backward-fill
  3. c) The proportion of the dataset’s variance captured by each principal component
  4. c) Encoding features with cyclical patterns, like day of the week
  5. c) They rely on model training to determine feature importance
  6. b) Principal Component Analysis (PCA)
  7. c) Correlation Thresholding
  8. c) When interaction effects between features need to be captured
  9. c) It may over-penalize features, potentially leading to underfitting
  10. b) To ensure that selected features generalize well to new data

Answers

  1. b) Variance Thresholding
  2. c) Reindexing the data to a regular frequency and using forward-fill or backward-fill
  3. c) The proportion of the dataset’s variance captured by each principal component
  4. c) Encoding features with cyclical patterns, like day of the week
  5. c) They rely on model training to determine feature importance
  6. b) Principal Component Analysis (PCA)
  7. c) Correlation Thresholding
  8. c) When interaction effects between features need to be captured
  9. c) It may over-penalize features, potentially leading to underfitting
  10. b) To ensure that selected features generalize well to new data

Answers

  1. b) Variance Thresholding
  2. c) Reindexing the data to a regular frequency and using forward-fill or backward-fill
  3. c) The proportion of the dataset’s variance captured by each principal component
  4. c) Encoding features with cyclical patterns, like day of the week
  5. c) They rely on model training to determine feature importance
  6. b) Principal Component Analysis (PCA)
  7. c) Correlation Thresholding
  8. c) When interaction effects between features need to be captured
  9. c) It may over-penalize features, potentially leading to underfitting
  10. b) To ensure that selected features generalize well to new data

Answers

  1. b) Variance Thresholding
  2. c) Reindexing the data to a regular frequency and using forward-fill or backward-fill
  3. c) The proportion of the dataset’s variance captured by each principal component
  4. c) Encoding features with cyclical patterns, like day of the week
  5. c) They rely on model training to determine feature importance
  6. b) Principal Component Analysis (PCA)
  7. c) Correlation Thresholding
  8. c) When interaction effects between features need to be captured
  9. c) It may over-penalize features, potentially leading to underfitting
  10. b) To ensure that selected features generalize well to new data
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