MLS-C01 exam dumps: questions 101 to 200 of 388

Correct answer: A

• A. Correct.

  Converting the 'Join_Date' into a numerical feature representing the number of days since the customer joined is appropriate because it captures a meaningful temporal relationship between the join date and customer churn, which can improve the model's predictive power.

• B. Incorrect.

  One-hot encoding the 'Join_Date' column would result in too many features if the dataset spans many unique dates, making it impractical and leading to a sparsity issue.

• C. Incorrect.

  Applying tokenization to the 'Join_Date' column is not suitable as it is generally used for text data and does not capture the temporal relationship inherent in a date.

• D. Incorrect.

  Removing the 'Join_Date' column discards potentially valuable information that could help the model understand temporal patterns related to customer churn.

Correct answers: A, B, D

• A. Correct.

  Creating a new feature for the house's age is a common practice in feature engineering, as it provides a more meaningful representation of the 'Year Built' feature for the model.

• B. Correct.

  Applying one-hot encoding to the 'Neighborhood' feature is appropriate because it is a categorical variable. One-hot encoding converts categories into numerical values, which are suitable for machine learning algorithms.

• C. Incorrect.

  Removing the 'Year Built' feature entirely is not justified unless there is evidence that it contributes no meaningful information or causes overfitting. In this case, it can be transformed into a more useful feature (e.g., house age).

• D. Correct.

  Grouping 'Neighborhood' categories based on median house prices can reduce the cardinality of the feature and capture useful information, which improves model performance.

• E. Incorrect.

  Applying PCA to the 'Year Built' feature is not appropriate because PCA is used for dimensionality reduction on high-dimensional numerical datasets, not individual features like 'Year Built'.

Correct answers: A, B, D, E

• A. Correct.

  One-hot encoding is appropriate for categorical features like 'Subscription Plan', as it allows the model to handle these non-numeric values effectively.

• B. Correct.

  Binning can help reduce the impact of outliers in the 'Lifetime Value' feature by grouping data into more interpretable ranges, which can improve model performance.

• C. Incorrect.

  Removing the 'Customer Activity Logs' feature entirely may lead to a loss of valuable information. Dimensionality reduction techniques like PCA are better suited for high-dimensional features.

• D. Correct.

  Principal Component Analysis (PCA) is an effective technique to reduce the dimensionality of the 'Customer Activity Logs' feature while retaining important information.

• E. Correct.

  Scaling numerical features such as 'Monthly Usage Hours' ensures that the model treats them appropriately, especially for algorithms sensitive to feature scaling like gradient descent.

Correct answers: A, B, D

• A. Correct.

  Correct. One-hot encoding is an appropriate method for handling a categorical feature like 'Subscription_Type' to ensure it is represented numerically without introducing ordinal relationships.

• B. Correct.

  Correct. Binning can help reduce the impact of extreme outliers by grouping continuous values into discrete intervals, which might be beneficial for certain algorithms or business interpretations.

• C. Incorrect.

  Incorrect. Ignoring outliers is not recommended as they can negatively impact the performance of machine learning models, especially algorithms sensitive to extreme values.

• D. Correct.

  Correct. Replacing outliers with the median value is a common technique to mitigate the impact of extreme values without losing too much information.

• E. Incorrect.

  Incorrect. Label encoding is not suitable in this case as it introduces ordinal relationships between categorical values (e.g., 'Free' < 'Basic' < 'Premium'), which do not exist naturally in 'Subscription_Type'.

Correct answers: A, B, C, D

• A. Correct.

  Imputing missing values is essential for ensuring that your dataset is complete for analysis and visualization. Choosing the appropriate imputation method (mean, median, or mode) depends on the feature type and its distribution.

• B. Correct.

  Using boxplots to identify outliers is a common exploratory data analysis (EDA) technique. Handling outliers can improve the quality of analysis and model performance.

• C. Correct.

  One-hot encoding is necessary to convert categorical variables into a numerical format, which is required for visualizations and machine learning algorithms.

• D. Correct.

  Log-transforming skewed numerical features helps normalize their distributions, improving interpretability in visualizations and reducing potential bias in modeling.

• E. Incorrect.

  Directly training a machine learning model on raw data without preprocessing is not recommended, as raw datasets often contain missing values, categorical variables, and other issues that hinder model performance.