You work for the credit card division of a large financial services firm. You are a machine learning specialist working on a credit card transaction classification model. Your model will be used to classify your firm’s customer transactions for use in direct marketing campaigns by your firm’s marketing department. You have built your model based on the SageMaker pre-built Linear Learner algorithm. You have also deployed your model to an inference endpoint using an inference pipeline. You perform your feature engineering via the SageMaker built-in feature transformers. So you don’t need to write your own feature engineering logic.
You have defined the containers for your pipeline using the CreateModel SageMaker API, and you have created an inference endpoint using the SageMaker CreateEndpointConfig and CreateEndpoint APIs. You have decided to change your pipeline to use a different SageMaker feature transformer strategy (change the strategy from the default None to SingleRecord). 
How do you make this change to your inference pipeline?

Answer options:

A.Your pipeline model is mutable, meaning you can change it while it is running.
B.Your pipeline is immutable, but you can update your inference pipeline by deleting the old one and redeploying the new one using the SageMaker CreateEndpointConfig and CreateEndpoint APIs.
C.Your pipeline is immutable, but you can change your inference pipeline by deploying a new one using the ReplaceEndpoint API.
D.Your pipeline is immutable, but you can change your inference pipeline by deploying a new one using the UpdateEndpoint API.

Answer: D
Option A is incorrect. SageMaker inference pipelines are immutable, so you cannot change them while they are running.
Option B is incorrect. It is true that your inference pipeline is immutable, but you change it via the UpdateEndpoint API. You do not have to delete your pipeline and recreate it.
Option C is incorrect. Your inference pipeline is immutable, but you change it via the UpdateEndpoint API, not a ReplaceEndpoint API. The is no ReplaceEndpoint API.
Option D is correct. Your inference pipeline is immutable. You change it by deploying a new one via the UpdateEndpoint API. SageMaker deploys the new inference pipeline, then switches incoming requests to the new one. SageMaker then deletes the resources associated with the old pipeline.
Reference:
Please see the Amazon SageMaker developer guide titled Deploy an Inference Pipeline, the Amazon SageMaker developer guide titled CreateModel, the Amazon SageMaker developer guide titled UpdateModel, the Amazon SageMaker developer guide titled Use Amazon SageMaker Built-in Algorithms, and the SageMaker docs page titled Transformer.