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MLA-C01 Practice Question: A data science team is using Amazon SageMaker to…
A data science team is using Amazon SageMaker to train and deploy a binary classification model. They want to continuously monitor the model for data drift in production. Which combination of AWS services and SageMaker features should they use to implement automated drift detection with minimal operational overhead?
⚠ Common exam trap
Many candidates confuse SageMaker Debugger (training debugging) with SageMaker Model Monitor (production drift detection), or they overcomplicate the solution by adding unnecessary services like Lambda or Config when the native integration with CloudWatch already provides automated alerting.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
SageMaker Model Monitor and Amazon CloudWatch
SageMaker Model Monitor is the native SageMaker feature designed specifically for continuously monitoring deployed models for data drift, bias drift, and feature attribution drift. It automatically captures inference requests and responses, computes statistics, and publishes metrics to Amazon CloudWatch, which can trigger alarms for drift detection. This combination provides automated drift detection with minimal operational overhead because it requires no custom infrastructure or manual scheduling.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
SageMaker Debugger and Amazon SNS
Why it's wrong here
Debugger captures training tensors and SNS sends notifications; neither compares production inference data to a baseline. It tempts because Debugger surfaces model anomalies and SNS alerts, which would suit training-job debugging notifications rather than SageMaker Model Monitor's scheduled drift checks.
- ✗
SageMaker Pipelines and AWS Lambda
Why it's wrong here
Pipelines orchestrates training workflows and Lambda runs event-driven code; neither computes drift statistics against a baseline. It tempts because Pipelines automates ML steps and Lambda triggers actions, which would fit retraining orchestration rather than SageMaker Model Monitor's built-in drift detection.
- ✗
SageMaker Clarify and AWS Config
Why it's wrong here
Clarify explains predictions and detects bias, while AWS Config tracks resource compliance; neither monitors live endpoint data drift. It tempts because Clarify analyses model behaviour, and would be correct for pre-deployment bias reports or explainability, not continuous production drift monitoring.
- ✓
SageMaker Model Monitor and Amazon CloudWatch
Why this is correct
SageMaker Model Monitor continuously evaluates production data against baselines to detect drift, publishing metrics and alerts to Amazon CloudWatch. This managed integration requires no custom infrastructure, satisfying the requirement for automated drift detection with minimal operational overhead.
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