The correct approach is B, which involves using SQL in BigQuery to apply one-hot encoding to the city column. This method efficiently converts categorical city names into binary columns, enabling the linear regression model to utilize city information as a predictive feature without unnecessary complexity.

• Why not A? Assigning cities to numerical regions may oversimplify the data, potentially diminishing the model's predictive accuracy by not fully capturing each city's unique characteristics.
• Why not C? Removing city information altogether would eliminate a significant predictive variable, likely reducing the model's effectiveness.
• Why not D? While TensorFlow offers powerful tools for handling categorical data, this approach introduces unnecessary complexity for the task at hand, as one-hot encoding can be directly accomplished within BigQuery using SQL.