AWS Certified Machine Learning Engineer – Associate (MLA-C01) — Question 72

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?

Answer options

• A. Deploy the models by using scheduled AWS Glue jobs. Use Amazon CloudWatch alarms to monitor the data quality and to send alerts.
• B. Deploy the models by using scheduled AWS Batch jobs. Use AWS CloudTrail to monitor the data quality and to send alerts.
• C. 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.
• D. Deploy the models by using Amazon SageMaker batch transform. Use SageMaker Model Monitor to monitor the data quality and to send alerts.

Correct answer: D

Explanation

The correct answer is D because Amazon SageMaker batch transform is specifically designed for deploying ML models and includes SageMaker Model Monitor, which provides the necessary tools for monitoring data quality and sending alerts. The other options either do not offer integrated monitoring solutions specifically tailored for ML models or do not support the asynchronous deployment requirement as effectively.