Topic 1 Question 66
A company has used Amazon SageMaker to deploy a predictive ML model in production. The company is using SageMaker Model Monitor on the model. After a model update, an ML engineer notices data quality issues in the Model Monitor checks. What should the ML engineer do to mitigate the data quality issues that Model Monitor has identified?
Adjust the model's parameters and hyperparameters.
Initiate a manual Model Monitor job that uses the most recent production data.
Create a new baseline from the latest dataset. Update Model Monitor to use the new baseline for evaluations.
Include additional data in the existing training set for the model. Retrain and redeploy the model.
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コメント(4)
- 正解だと思う選択肢: D
the model needs to be retrained
👍 2Ell892024/12/31 - 正解だと思う選択肢: C
If the problems start appearing "After a model update" then C is the only valid option.
👍 1GiorgioGss2024/11/27 - 正解だと思う選択肢: C
Model Monitor gives data quality issues --> Create new baseline --> Validate baseline --> Update Model Monitor with new baseline --> Reevaluate data quality --> Investigate and fix root cause (if issues persist) --> Monitor continuously
👍 1Saransundar2024/12/04
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