MLS-C01 · Question #285
A music streaming company is building a pipeline to extract features. The company wants to store the features for offline model training and online inference. The company wants to track feature histor
The correct answer is A. Use Amazon SageMaker Feature Store to store features for model training and inference. Create. SageMaker Feature Store consists of an online and an offline mode for managing features. The online store is used for low-latency real-time inference use cases. The offline store is primarily used for batch predictions and model training. https://aws.amazon.com/blogs/machine-lear
Question
A music streaming company is building a pipeline to extract features. The company wants to store the features for offline model training and online inference. The company wants to track feature history and to give the company's data science teams access to the features. Which solution will meet these requirements with the MOST operational efficiency?
Options
- AUse Amazon SageMaker Feature Store to store features for model training and inference. Create
- BUse Amazon SageMaker Feature Store to store features for model training and inference. Create
- CCreate one Amazon S3 bucket to store online inference features. Create a second S3 bucket to
- DCreate two separate Amazon DynamoDB tables to store online inference features and offline
How the community answered
(30 responses)- A73% (22)
- B7% (2)
- C3% (1)
- D17% (5)
Explanation
SageMaker Feature Store consists of an online and an offline mode for managing features. The online store is used for low-latency real-time inference use cases. The offline store is primarily used for batch predictions and model training. https://aws.amazon.com/blogs/machine-learning/speed-ml-development-using-sagemaker- feature-store-and-apache-iceberg-offline-store-compaction/
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