MLA-C01 · Question #222
An ML engineer is building a logistic regression model to predict customer churn for subscription services. The ML engineer is using a dataset that contains two string variables: location and job_seni
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Question
An ML engineer is building a logistic regression model to predict customer churn for subscription services. The ML engineer is using a dataset that contains two string variables: location and job_seniority_level. The location variable has 3 distinct values, and the job_seniority_level variable has over 10 distinct values. The ML engineer must perform preprocessing on the variables. Which solution will meet this requirement?
Options
- AApply tokenization to location. Apply ordinal encoding to job_seniority_level.
- BApply one-hot encoding to location. Apply ordinal encoding to job_seniority_level
- CApply binning to location. Apply standard scaling to job_seniority_level.
- DApply one-hot encoding to location. Apply standard scaling to job_seniority_level.
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