AWS Certified Machine Learning – Specialty — Question 243

A machine learning (ML) engineer at a bank is building a data ingestion solution to provide transaction features to financial ML models. Raw transactional data is available in an Amazon Kinesis data stream.

The solution must compute rolling averages of the ingested data from the data stream and must store the results in Amazon SageMaker Feature Store. The solution also must serve the results to the models in near real time.

Which solution will meet these requirements?

Answer options

• A. Load the data into an Amazon S3 bucket by using Amazon Kinesis Data Firehose. Use a SageMaker Processing job to aggregate the data and to load the results into SageMaker Feature Store as an online feature group.
• B. Write the data directly from the data stream into SageMaker Feature Store as an online feature group. Calculate the rolling averages in place within SageMaker Feature Store by using the SageMaker GetRecord API operation.
• C. Consume the data stream by using an Amazon Kinesis Data Analytics SQL application that calculates the rolling averages. Generate a result stream. Consume the result stream by using a custom AWS Lambda function that publishes the results to SageMaker Feature Store as an online feature group.
• D. Load the data into an Amazon S3 bucket by using Amazon Kinesis Data Firehose. Use a SageMaker Processing job to load the data into SageMaker Feature Store as an offline feature group. Compute the rolling averages at query time.

Correct answer: C

Explanation

Option C is correct because it utilizes Amazon Kinesis Data Analytics to compute rolling averages in real time and then publishes the results to SageMaker Feature Store, meeting the near real-time requirement. Options A and D involve unnecessary data transfers to S3 and do not provide real-time capabilities. Option B suggests calculating rolling averages in SageMaker Feature Store, which cannot be done directly within the feature store.