AWS Certified Machine Learning Engineer – Associate (MLA-C01) — Question 120

A company is using Amazon EMR. The company has a large dataset in Amazon S3 that needs to be ingested into Amazon SageMaker Feature Store. The dataset contains historical data and real-time streaming data.

The company must ensure that the Feature Store online store is updated with the most recent data as soon as the data becomes available. The company also must maintain a complete Feature Store offline store for batch processing.

Which solution will meet these requirements?

Answer options

• A. Use the PutRecord API in Feature Store Runtime to ingest all the data into the online store.
• B. Use the PutRecord API in Feature Store Runtime to ingest all the data into the offline store.
• C. Use the Feature Store Spark connector to ingest the data as Spark DataFrames with the online store and offline store enabled.
• D. Use the Feature Store Spark connector to ingest the data as Spark DataFrames with only the online store enabled.

Correct answer: C

Explanation

Option C is correct because it allows for the simultaneous ingestion of both online and offline stores, ensuring that the Feature Store is updated in real-time while also maintaining historical data for batch processing. Option A only updates the online store, while Option B only focuses on the offline store, neither of which meets the full requirement. Option D limits ingestion to just the online store, thus failing to cover the need for a complete offline store.