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Crucial Exams

CompTIA Data+ DA0-002 (V2) Practice Question

A data analyst is preparing a 250 000-row customer data set to train a supervised churn-prediction model. The target column, Churn_Flag, contains Yes/No values for 248 700 customers, while the remaining 1 300 rows have NULL in that column only; every feature in those 1 300 rows is otherwise complete and within expected ranges. Exploratory checks show that dropping 1 300 records will not materially change the class balance or statistical power of the model. The machine-learning library being used will raise an error if the target variable is missing. Which data-cleansing technique is MOST appropriate for handling the 1 300 affected rows before modeling?

  • Bin Churn_Flag into broader categories and keep the rows to maximize training data size.

  • Impute each missing Churn_Flag with the most common class so the overall distribution is preserved.

  • Delete the 1 300 rows that have a NULL value in Churn_Flag before training the model.

  • Apply min-max scaling to the numeric features so the algorithm can ignore the NULL labels.

CompTIA Data+ DA0-002 (V2)
Data Acquisition and Preparation
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