You work for a company that manufactures cell phone peripherals such as bluetooth headphones and bluetooth selfie sticks. Your company has designed its products to act as IoT devices that send usage and diagnostic MQTT messages to your IoT Core service running in AWS. Your machine learning team wants to use this IoT message data to run inferences through your machine learning inference end-point. However, the IoT data is unstructured. So you need to preprocess the data by performing feature engineering on the observations before they are fed into your inference endpoint.
You have decided to use a SageMaker Inference Pipeline to construct this machine learning solution. As you define the containers for your pipeline, one for feature engineering pre-processing, and one for inference predictions, which SageMaker CLI command and which parameter on that command do you need to run using the SageMaker CLI to build your inference pipeline?

Answer options:

A.CreateModel command with the EndpointArn request parameter
B.UpdateEndpoint command with the Containers parameter
C.CreateModel command with the PrimaryContainer request parameter
D.CreateModel command with the Containers request parameter
E.UpdateEndpoint command with the ModelArn parameter

Answer: D
Option A is incorrect. The SageMaker CLI CreateModel command is the correct command. But EndpointArn is a response element of the UpdateEndpoint command. 
Option B is incorrect. The SageMaker CLI UpdateEndpoint command is used to switch from an existing endpoint to a new endpoint. You would not use this command to create a new inference pipeline container.
Option C is incorrect. The SageMaker CLI CreateModel command is the correct command. But you use the thePrimaryContainer request parameter when you want to create a single container, not when you want to create an inference pipeline.
Option D is correct. The SageMaker CLI CreateModel command is the correct command, and the Containers parameter is the correct parameter. The Containers request parameter is used to set the containers that make up your pipeline.
Option E is incorrect. The SageMaker CLI UpdateEndpoint command is used to switch from an existing endpoint to a new endpoint. You would not use this command to create a new inference pipeline container.
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 UpdateEndpoint, the AWS CLI Command Reference titled create-model, and the AWS IoT Core overview page.