MLA-C01 · Question #183
A music streaming company constantly streams song ratings from an application to an Amazon S3 bucket. The company wants to use the ratings as an input for training and inference of an Amazon…
The correct answer is A. Ingest data into SageMaker Feature Store from the S3 bucket. Apply tags and indexes. Amazon SageMaker Feature Store provides a centralized repository for ML features that supports both batch training and low-latency real-time inference while keeping features synchronized between offline and online stores. Ingesting data from Amazon S3 into Feature Store ensures…
Question
A music streaming company constantly streams song ratings from an application to an Amazon S3 bucket. The company wants to use the ratings as an input for training and inference of an Amazon SageMaker AI model. The company has an AWS Glue Data Catalog that is configured with the S3 bucket as the source. An ML engineer needs to implement a solution to create a repository for this data. The solution must ensure that the data stays synchronized during batch training and real-time inference. Which solution will meet these requirements?
Options
- AIngest data into SageMaker Feature Store from the S3 bucket. Apply tags and indexes.
- BUse Amazon Athena. Create tables by using CREATE TABLE AS SELECT (CTAS) queries to
- CUse AWS Lake Formation. Apply tag-based control on the data.
- DUse the Generate Data Insights function in SageMaker Data Wrangler.
How the community answered
(24 responses)- A83% (20)
- B4% (1)
- C4% (1)
- D8% (2)
Explanation
Amazon SageMaker Feature Store provides a centralized repository for ML features that supports both batch training and low-latency real-time inference while keeping features synchronized between offline and online stores. Ingesting data from Amazon S3 into Feature Store ensures consistent feature definitions and values across training and inference workflows.
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