AWS Certified Cloud Practitioner
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Q5. A manufacturer wants to detect model drift in production and receive alerts when incoming data changes significantly from training data. Which SageMaker service should they choose?
Real Exam
Community
RRitesh
0
A
SageMaker Model Monitor
B
SageMaker Canvas
C
SageMaker Neo
D
SageMaker Ground Truth
Explanation:
Explanation
SageMaker Model Monitor is the correct choice because:
-
Purpose-built for model monitoring: SageMaker Model Monitor is specifically designed to detect model drift and data quality issues in production environments.
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Data drift detection: It automatically detects when incoming data deviates from the training data distribution, which is exactly what the manufacturer needs.
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Alerting capabilities: Model Monitor can send alerts when drift is detected, allowing teams to take corrective actions.
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Real-time monitoring: It monitors models in real-time or batch mode, providing continuous visibility into model performance.
Why other options are incorrect:
- SageMaker Canvas (B): This is a no-code tool for building ML models, not for monitoring production models.
- SageMaker Neo (C): This is a compiler that optimizes ML models for different hardware platforms, not for monitoring drift.
- SageMaker Ground Truth (D): This is a data labeling service for creating training datasets, not for monitoring production models.
Key AWS Service: Amazon SageMaker Model Monitor helps maintain model quality in production by detecting concept drift, data drift, and quality issues, ensuring models continue to perform accurately over time.
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