Question

A machine learning team at a fraud-detection company uses Amazon SageMaker Feature Store. They need to serve engineered features with single-digit millisecond latency to a real-time inference endpoint that scores credit card transactions as they occur. Separately, a data science team needs to query the full history of those same features to build training datasets and run batch analytics with Amazon Athena. Which configuration best meets both requirements?

Accepted Answer

Create the feature group with both the online store and the offline store enabled, serving low-latency lookups from the online store and running historical queries against the offline store in S3.

Answer

Create the feature group with only the offline store enabled and query it directly from the inference endpoint for each transaction.

Answer

Create the feature group with only the online store enabled and use Amazon Athena to query the online store for historical training datasets.

Answer

Store all features in a single Amazon RDS instance and add a read replica to serve the real-time inference endpoint.