QuestionQ24
Implement machine learning model lifecycle and operationsA data science team trains a model that relies on features stored in a managed feature store.
The model is registered in Azure Machine Learning and will be deployed to a real-time endpoint.
After deployment, the model must:
- Retrieve feature values dynamically at inference time.
- Use the same feature definitions used during training.
- Run without manual configuration changes across environments.
You need to define feature store entities so feature retrieval works as expected when the deployed model runs.
Which feature store entity should be selected for each requirement? Each entity may be used once, multiple times, or not at all.
Drag & Drop
Feature retrieval specification
Feature set specification
Feature set asset
Feature set materialization
Define how features are retrieved at inference.
Ensure feature consistency between training and inference.
Enable automated feature lookup in production.
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