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PROFESSIONAL-MACHINE-LEARNING-ENGINEER · Question #100

You work for a magazine distributor and need to build a model that predicts which customers will renew their subscriptions for the upcoming year. Using your company's historical data as your training

The correct answer is C. Use the AI Explanations feature on AI Platform. Submit each prediction request with the 'explain'. https://cloud.google.com/ai-platform/prediction/docs/ai-explanations/overview AI Explanations helps you understand your model's outputs for classification and regression tasks. Whenever you request a prediction on AI Platform, AI Explanations tells you how much each feature in th

Submitted by stefanr· Apr 18, 2026Monitoring, optimizing, and maintaining ML solutions

Question

You work for a magazine distributor and need to build a model that predicts which customers will renew their subscriptions for the upcoming year. Using your company's historical data as your training set, you created a TensorFlow model and deployed it to AI Platform. You need to determine which customer attribute has the most predictive power for each prediction served by the model. What should you do?

Options

  • AUse AI Platform notebooks to perform a Lasso regression analysis on your model, which will
  • BStream prediction results to BigQuery. Use BigQuery's CORR(X1, X2) function to calculate the
  • CUse the AI Explanations feature on AI Platform. Submit each prediction request with the 'explain'
  • DUse the What-If tool in Google Cloud to determine how your model will perform when individual

How the community answered

(58 responses)
  • A
    5% (3)
  • B
    16% (9)
  • C
    71% (41)
  • D
    9% (5)

Explanation

https://cloud.google.com/ai-platform/prediction/docs/ai-explanations/overview AI Explanations helps you understand your model's outputs for classification and regression tasks. Whenever you request a prediction on AI Platform, AI Explanations tells you how much each feature in the data contributed to the predicted result.

Topics

#AI Platform#Model explainability#Feature attribution#Post-deployment analysis

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