PROFESSIONAL-MACHINE-LEARNING-ENGINEER · Question #336
PROFESSIONAL-MACHINE-LEARNING-ENGINEER Question #336: Real Exam Question with Answer & Explanation
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Question
You are building an ML model to predict customer churn for a subscription service. You have trained your model on Vertex AI using historical data, and deployed it to a Vertex AI endpoint for real-time predictions. After a few weeks, you notice that the model's performance, measured by AUC (area under the ROC curve), has dropped significantly in production compared to its performance during training. How should you troubleshoot this problem?
Options
- AMonitor the training/serving skew of feature values for requests sent to the endpoint.
- BMonitor the resource utilization of the endpoint, such as CPU and memory usage, to identify
- CEnable Vertex Explainable AI feature attribution to analyze model predictions and understand the
- DMonitor the latency of the endpoint to determine whether predictions are being served within the
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