You work as a machine learning specialist for a software company that offers real-time interactive sports viewing app for mobile phones and tablets. You gather real-time streaming sports statistics and game action data and use the streaming data to produce real-time analytics and active predictions of the likely outcome of the game. To produce your prediction, you need to use several machine learning models that use the real-time streaming data as their training and inference data sources. Since the real-time streaming game data is delivered from several different sources, the format and schema of the data need transformation and sanitation. Which option is the most efficient way to perform the feature engineering of your real-time streaming data for use in your training and inference requests?

Answer options:

A.Ingest the real-time streaming data using Kinesis Data Firehose using the kinesis-firehose-process-record Lambda blueprint for transformation. Stream the output of your Kinesis Data Firehose into SageMaker offline feature store FeatureGroup.
B.Ingest the real-time streaming data using Kinesis Data Firehose using the kinesis-process-record Lambda blueprint for transformation. Stream the output of your Kinesis Data Firehose into SageMaker offline feature store FeatureGroup.
C.Ingest the real-time streaming data using Kinesis Data Firehose using the kinesis-firehose-process-record Lambda blueprint for transformation. Stream the output of your Kinesis Data Firehose into SageMaker offline and online feature store FeatureGroups.
D.Ingest the real-time streaming data using Kafka using the kinesis-process-record Lambda blueprint for transformation. Stream the output of your Kinesis Data Firehose into SageMaker online feature store FeatureGroup.

Correct Answer: C
Option A is incorrect. You can ingest your streaming data using Kinesis Data Firehose and use the kinesis-firehose-process-record Lambda blueprint for transformation. However, you need to stream the output of your Kinesis Data Firehose into both the offline and online feature store FeatureGroups since you wish to train using your Feature Store groups and produce real-time inferences using your online Feature Store FeatureGroup.
Option B is incorrect. You can ingest your streaming data using Kinesis Data Firehose, and you could use the kinesis-process-record Lambda blueprint for transformation. However, you need to stream the output of your Kinesis Data Firehose into both the offline and online feature store FeatureGroups since you wish to train using your offline Feature Store groups and produce real-time inferences using your online Feature Store FeatureGroup.
Option C is correct. You can ingest your streaming data using Kinesis Data Firehose and use the kinesis-firehose-process-record Lambda blueprint for transformation. You will also want to stream the output of your Kinesis Data Firehose into both the offline and online feature store FeatureGroups since you wish to train using your offline Feature Store groups and produce real-time inferences using your online Feature Store FeatureGroup.
Option D is incorrect. You can ingest your streaming data using Kafka, and you could use the kinesis-process-record Lambda blueprint for transformation. However, you need to stream the output of your Kinesis Data Firehose into both the offline and online feature store FeatureGroups since you wish to train using your offline Feature Store groups and produce real-time inferences using your online Feature Store FeatureGroup.
References:
Please see the AWS Machine Learning blog titled Understanding the key capabilities of Amazon SageMaker Feature Store (https://aws.amazon.com/blogs/machine-learning/understanding-the-key-capabilities-of-amazon-sagemaker-feature-store/),
The Amazon SageMaker page titled Amazon SageMaker Feature Store (https://aws.amazon.com/sagemaker/feature-store/),
The Amazon SageMaker developer guide titled Create, Store, and Share Features with Amazon SageMaker Feature Store (https://docs.aws.amazon.com/sagemaker/latest/dg/feature-store.html),
The Amazon SageMaker developer guide titled Create Feature Groups (https://docs.aws.amazon.com/sagemaker/latest/dg/feature-store-create-feature-group.html),
The Amazon SageMaker Examples page titled Fraud Detection with Amazon SageMaker FeatureStore (https://sagemaker-examples.readthedocs.io/en/latest/sagemaker-featurestore/sagemaker_featurestore_fraud_detection_python_sdk.html),
The Amazon Kinesis Data Firehose developer guide titled Amazon Kinesis Data Firehose Data Transformation (https://docs.aws.amazon.com/firehose/latest/dev/data-transformation.html)