A company uses Amazon Bedrock with a custom model deployed via Amazon SageMaker. They want to monitor for data drift in input prompts over time. Which AWS service is best suited for this?
Model Monitor can be configured to capture input data and detect drift using statistical methods.
Why this answer
Amazon SageMaker Model Monitor is the correct choice because it is specifically designed to detect data drift in machine learning models, including input prompts for custom models deployed via SageMaker. It continuously monitors the distribution of input data against a baseline and alerts when drift occurs, which aligns with the requirement to monitor input prompts over time.
Exam trap
The trap here is that candidates often confuse general monitoring services like CloudWatch with specialized ML monitoring tools, assuming CloudWatch can handle data drift detection when it actually lacks the statistical analysis required for such tasks.
How to eliminate wrong answers
Option A is wrong because Amazon CloudWatch is a monitoring service for AWS resources and applications (e.g., metrics, logs, alarms), but it does not have built-in capabilities to detect data drift in ML model inputs. Option C is wrong because AWS CloudTrail records API activity for auditing and governance, not for monitoring data drift in model inputs. Option D is wrong because Amazon Athena is an interactive query service for analyzing data in S3 using SQL, not a monitoring tool for data drift.