MLA-C01 · Question #173
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…
The correct answer is C. Use SageMaker Model Monitor to detect data drift. Use an AWS Lambda function to automate the. SageMaker Model Monitor is designed to detect data drift by comparing production data against baseline statistics, and it can trigger an AWS Lambda function to automatically start a retraining job when drift is detected.
Question
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?
Options
- AUse an AWS Glue crawler and an AWS Glue extract, transform and load (ETL) job to detect data
- BUse Amazon Managed Service for Apache Flink to detect data drift. Use an AWS Lambda
- CUse SageMaker Model Monitor to detect data drift. Use an AWS Lambda function to automate the
- DUse Amazon QuickSight anomaly detection to detect data drift. Use an AWS Step Functions
How the community answered
(36 responses)- A8% (3)
- B14% (5)
- C75% (27)
- D3% (1)
Explanation
SageMaker Model Monitor is designed to detect data drift by comparing production data against baseline statistics, and it can trigger an AWS Lambda function to automatically start a retraining job when drift is detected.
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