You work for a flight diagnostics company that builds instrumentation for airline manufacturers. Your company’s instrumentation hardware and software are used to detect flight pattern information such as flight path deviation and airline component malfunction. Your team of machine learning specialists has created a model using the Random Cut Forest algorithm to be used to identify anomalies in the data. The streaming data that your instrumentation processes need to be cleaned and transformed via feature engineering before passing it to your inference endpoint. You have created the pre-processing and post-processing steps (for cleaning and feature engineering) in your training process.
How can you implement the cleaning and feature engineering steps in your inference processing in the most efficient manner?

Answer options:

A.Execute the pre-processing in a client application before sending the data to your inference endpoint.
B.Bundle and export the training pre-processing steps and deploy them to your inference container.
C.Bundle and export the training pre-processing steps and deploy them as part of your Inference Pipeline.
D.Bundle and export the training pre-processing steps and deploy them to IoT Core on the data emitting devices.

Answer: C
Option A is incorrect. Although you could execute your pre-processing steps in a client application before sending the data to your inference end-point, this would require additional work on your part to build that client application and then incorporate your feature engineering scripts from your training process into it.
Option B is incorrect. You could also include your pre-processing steps in your inference container. However, this requires more work on your part than using the SageMaker Inference Pipelines feature.
Option C is correct. SageMaker Inference Pipelines allows you to bundle and export your pre and post-processing steps from your training process and deploy them as part of your Inference Pipeline. AWS fully manages inference Pipelines.
Option D is incorrect. Amazon IoT Core is used to facilitate device intercommunication. It is not a service you would use for pre-processing data streams for machine learning inference endpoints. 
Reference:
Please see the Amazon announcement titled Announcing Enhancements for Data Processing and Feature Engineering, and Improved Framework Support with Amazon SageMaker, the Amazon SageMaker developer guide titled Deploy an Inference Pipeline, the AWS Machine Learning blog titled Use the built-in Amazon SageMaker Random Cut Forest algorithm for anomaly detection, and the AWS IoT Core Overview page.