AWS Certified Machine Learning Engineer – Associate (MLA-C01) — Question 165

An ML engineer is setting up an Amazon SageMaker AI pipeline for an ML model. The pipeline must automatically initiate a re-training job if any data drift is detected.

How should the ML engineer set up the pipeline to meet this requirement?

Answer options

• A. Use an AWS Glue crawler and an AWS Glue extract, transform and load (ETL) job to detect data drift. Use AWS Glue triggers to automate the re-training job.
• B. Use Amazon Managed Service for Apache Flink to detect data drift. Use an AWS Lambda function to automate the re-training job.
• C. Use SageMaker Model Monitor to detect data drift. Use an AWS Lambda function to automate the re-training job.
• D. Use Amazon QuickSight anomaly detection to detect data drift. Use an AWS Step Functions workflow to automate the re-training job.

Correct answer: C

Explanation

The correct answer is C because SageMaker Model Monitor is specifically designed to detect data drift in machine learning models and can seamlessly integrate with AWS Lambda for automating re-training jobs. The other options either use inappropriate services for detecting data drift or lack the direct integration with SageMaker for model re-training.