Topic 1 Question 76
An ML engineer needs to deploy ML models to get inferences from large datasets in an asynchronous manner. The ML engineer also needs to implement scheduled monitoring of the data quality of the models. The ML engineer must receive alerts when changes in data quality occur. Which solution will meet these requirements?
Deploy the models by using scheduled AWS Glue jobs. Use Amazon CloudWatch alarms to monitor the data quality and to send alerts.
Deploy the models by using scheduled AWS Batch jobs. Use AWS CloudTrail to monitor the data quality and to send alerts.
Deploy the models by using Amazon Elastic Container Service (Amazon ECS) on AWS Fargate. Use Amazon EventBridge to monitor the data quality and to send alerts.
Deploy the models by using Amazon SageMaker batch transform. Use SageMaker Model Monitor to monitor the data quality and to send alerts.
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コメント(2)
- 正解だと思う選択肢: D
https://docs.aws.amazon.com/sagemaker/latest/dg/model-monitor.html Model Monitor tracks data quality, model quality, bias drift, and feature attribution drift for production models. Model monitor setup with continuous monitoring with batch transform will work
👍 2Saransundar2024/12/03 - 正解だと思う選択肢: D
A is not valid. Monitoring data quality in model is done by model monitor, not by cloudwatch.
👍 1GiorgioGss2024/11/28
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