Engineer Features for Models SNOWPRO-ADVANCED-DATA-SCIENTIST Practice Questions (Page 5)

Questions 21–25

1. 21foundation · easy

   Which of the following is an example of a lagged feature in time series feature engineering?

   • AThe value of a variable from the previous time step
   • BThe mean of the variable over the entire dataset
   • CThe standard deviation of the variable across all rows
   • DThe one-hot encoded version of the variable
   Reveal Solution Discussion Select an answer first
2. 22foundation · easy

   What is the main advantage of TF-IDF over simple bag-of-words?

   • AIt preserves the order of words in the document
   • BIt down-weights common words that appear in many documents
   • CIt automatically handles missing values in the text
   • DIt creates dense vector representations of words
   Reveal Solution Discussion Select an answer first
4. 23foundation · easy

   Which of the following is a common strategy for handling missing values in a numeric feature?

   • APerforming PCA on the feature
   • BScaling the feature to [0, 1]
   • CApplying one-hot encoding to the feature
   • DImputing with the mean or median of the feature
   Reveal Solution Discussion Select an answer first
5. 24foundation · easy

   Which numerical transformation is most appropriate for reducing the impact of extreme outliers in a positively skewed feature?

   • AStandardization (z-score)
   • BLog transformation
   • CLabel encoding
   • DOne-hot encoding
   Reveal Solution Discussion Select an answer first
6. 25foundation · easy

   A data scientist wants to create a feature that captures the total sales per customer from a transaction table. Which operation best describes this feature creation?

   • ADropping the transaction date column
   • BOne-hot encoding of the customer ID column
   • CNormalization of the transaction amount column
   • DAggregation (e.g., SUM) grouped by customer
   Reveal Solution Discussion Select an answer first