Google Professional Machine Learning Engineer
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You work for a telecommunications company, and you're tasked with building a machine learning model to predict which customers may fail to pay their next phone bill. The goal of this model is to proactively identify at-risk customers and offer them assistance such as service discounts and bill deadline extensions to mitigate the risk of non-payment. The dataset for model training is stored in BigQuery and includes the following predictive features: - Customer_id - Age - Salary (measured in local currency) - Sex - Average bill value (measured in local currency) - Number of phone calls in the last month (integer) - Average duration of phone calls (measured in minutes). Your mandate includes ensuring the model does not exhibit bias against disadvantaged groups while maintaining high accuracy. What approach should you take to achieve this?_
A
Determine whether there is a meaningful correlation between the sensitive features and the other features. Train a BigQuery ML boosted trees classification model and exclude the sensitive features and any meaningfully correlated features.
B
Train a BigQuery ML boosted trees classification model with all features. Use the ML.GLOBAL_EXPLAIN method to calculate the global attribution values for each feature of the model. If the feature importance value for any of the sensitive features exceeds a threshold, discard the model and train without this feature._
C
Train a BigQuery ML boosted trees classification model with all features. Use the ML.EXPLAIN_PREDICT method to calculate the attribution values for each feature for each customer in a test set. If for any individual customer, the importance value for any feature exceeds a predefined threshold, discard the model and train the model again without this feature._
D
Define a fairness metric that is represented by accuracy across the sensitive features. Train a BigQuery ML boosted trees classification model with all features. Use the trained model to make predictions on a test set. Join the data back with the sensitive features, and calculate a fairness metric to investigate whether it meets your requirements.