MLS-C01 · Question #250
A data scientist at a financial services company used Amazon SageMaker to train and deploy a model that predicts loan defaults. The model analyzes new loan applications and predicts the risk of loan…
The correct answer is A. Use SageMaker Pipelines to create an automated workflow that extracts fresh data, trains the B. Configure SageMaker Model Monitor with an accuracy threshold to check for model drift. Initiate. https://aws.amazon.com/blogs/machine-learning/automate-model-retraining-with-amazon- sagemaker-pipelines-when-drift-is-detected/
Question
A data scientist at a financial services company used Amazon SageMaker to train and deploy a model that predicts loan defaults. The model analyzes new loan applications and predicts the risk of loan default. To train the model, the data scientist manually extracted loan data from a database. The data scientist performed the model training and deployment steps in a Jupyter notebook that is hosted on SageMaker Studio notebooks. The model's prediction accuracy is decreasing over time. Which combination of steps is the MOST operationally efficient way for the data scientist to maintain the model's accuracy? (Choose two.)
Options
- AUse SageMaker Pipelines to create an automated workflow that extracts fresh data, trains the
- BConfigure SageMaker Model Monitor with an accuracy threshold to check for model drift. Initiate
- CStore the model predictions in Amazon S3. Create a daily SageMaker Processing job that reads
- DRerun the steps in the Jupyter notebook that is hosted on SageMaker Studio notebooks to retrain
- EExport the training and deployment code from the SageMaker Studio notebooks into a Python
How the community answered
(49 responses)- A71% (35)
- C4% (2)
- D18% (9)
- E6% (3)
Explanation
https://aws.amazon.com/blogs/machine-learning/automate-model-retraining-with-amazon- sagemaker-pipelines-when-drift-is-detected/
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