An ML engineer is deploying a trained model to an Amazon SageMaker endpoint. The ML engineer needs to receive alerts when data quality issues occur in production. Which solution will meet this require

Question

Options

• AConfigure an Amazon CloudWatch metric alarm and a corresponding action to send an Amazon
• BIntegrate the SageMaker endpoint with a SageMaker Clarify processing job. Configure an
• CConfigure a monitoring job in SageMaker Model Monitor. Integrate Model Monitor with Amazon
• DConfigure a data flow in SageMaker Data Wrangler. Integrate Data Wrangler with Amazon

• A
  4% (1)
• B
  4% (1)
• C
  81% (22)
• D
  11% (3)

Explanation

SageMaker Model Monitor (C) is the purpose-built AWS service for continuously monitoring deployed endpoints for data quality drift, model quality issues, bias drift, and feature attribution drift - it integrates natively with Amazon EventBridge (or SNS) to trigger alerts when violations are detected against a baseline.

Why the distractors fail:

• (A) CloudWatch metric alarm alone can alert on infrastructure metrics (latency, invocations, errors) but has no built-in understanding of data quality - it cannot inspect input/output distributions for drift without Model Monitor feeding it the data quality metrics first.
• (B) SageMaker Clarify is designed for bias detection and explainability analysis, not continuous data quality monitoring of live endpoint traffic; it runs as a processing job, not a persistent monitoring schedule.
• (D) SageMaker Data Wrangler is a data preparation and feature engineering tool used before training, not for monitoring production endpoint traffic.

Memory tip: Think of the word "Monitor" literally - when you need to monitor a production model for quality issues, Model Monitor is your answer. Everything else in the distractors serves a different lifecycle stage: Clarify = fairness/explainability, Data Wrangler = preprocessing, CloudWatch = infrastructure metrics.

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