AWS Certified Cloud Practitioner
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A financial analytics team wants to store engineered features (like "credit utilization ratio") for reuse across multiple ML models. Which SageMaker component is designed for this purpose?
Real Exam
Community
RRitesh
A
SageMaker Ground Truth
B
SageMaker Feature Store
C
SageMaker Studio
D
SageMaker JumpStart
Explanation:
Explanation:
Amazon SageMaker Feature Store is specifically designed for storing, sharing, and managing machine learning features across multiple models and teams. It serves as a centralized repository for feature data, enabling:
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Feature Reuse: Once features like "credit utilization ratio" are engineered and stored, they can be reused across different ML models without recalculating them.
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Consistency: Ensures all models use the same feature values, maintaining consistency in predictions.
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Feature Discovery: Teams can discover and use features created by other teams.
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Online/Offline Serving: Supports both low-latency online inference and batch processing for training.
Why other options are incorrect:
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A) SageMaker Ground Truth: Used for data labeling and annotation, not feature storage.
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C) SageMaker Studio: An integrated development environment (IDE) for ML, not specifically for feature storage.
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D) SageMaker JumpStart: A hub for pre-built solutions and models, not designed for feature storage and management.
For financial analytics teams working with features like credit utilization ratios across multiple models, SageMaker Feature Store provides the ideal solution for feature governance, discovery, and reuse.
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