AWS Certified AI Practitioner AIF-C01 Practice Question
After a machine learning model is deployed, a company wants to detect when live data drifts away from the original training data and automatically launch a new training job. Which MLOps practice directly addresses this requirement?
Running periodic offline batch inference jobs
Encrypting model artifacts stored in Amazon S3
One-time hyperparameter tuning during the first training cycle
Continuous model monitoring with automated retraining pipelines
Continuous model monitoring compares live traffic with baseline training data to catch data or concept drift. When drift exceeds a defined threshold, the monitoring system can invoke an automated pipeline that retrains and redeploys the model. Initial hyperparameter tuning, one-time batch predictions, and encrypting artifacts are useful tasks, but none of them continuously watch production data or trigger retraining when drift occurs.
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AWS Certified AI Practitioner AIF-C01
Fundamentals of AI and ML
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