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MLA-C01 · Question #15

A company has deployed an XGBoost prediction model in production to predict if a customer is likely to cancel a subscription. The company uses Amazon SageMaker Model Monitor to detect deviations in th

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ML Solution Monitoring, Maintenance, and Security

Question

A company has deployed an XGBoost prediction model in production to predict if a customer is likely to cancel a subscription. The company uses Amazon SageMaker Model Monitor to detect deviations in the F1 score. During a baseline analysis of model quality, the company recorded a threshold for the F1 score. After several months of no change, the model's F1 score decreases significantly. What could be the reason for the reduced F1 score?

Options

  • AConcept drift occurred in the underlying customer data that was used for predictions.
  • BThe model was not sufficiently complex to capture all the patterns in the original baseline data.
  • CThe original baseline data had a data quality issue of missing values.
  • DIncorrect ground truth labels were provided to Model Monitor during the calculation of the

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Topics

#Model Monitoring#Concept Drift#F1 Score#Amazon SageMaker Model Monitor
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