Google Professional Machine Learning Engineer
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Ultimate access to all questions.
You work for a magazine distributor and are tasked with building a machine learning model to predict which customers will renew their subscriptions for the upcoming year. Using your company's historical customer data as your training set, you have created a TensorFlow model and deployed it to Google Cloud's AI Platform for serving predictions. Your goal is to determine which customer attribute (e.g., age, location, subscription length) has the most predictive power for each individual prediction made by the model. What should you do?
A
Use AI Platform notebooks to perform a Lasso regression analysis on your model, which will eliminate features that do not provide a strong signal.
B
Stream prediction results to BigQuery. Use BigQuery’s CORR(X1, X2) function to calculate the Pearson correlation coefficient between each feature and the target variable.
C
Use the AI Explanations feature on AI Platform. Submit each prediction request with the ‘explain’ keyword to retrieve feature attributions using the sampled Shapley method.
D
Use the What-If tool in Google Cloud to determine how your model will perform when individual features are excluded. Rank the feature importance in order of those that caused the most significant performance drop when removed from the model.