You work for a manufacturing firm that is attempting to build a rechargeable battery that has a capacity multiple times greater than the current rechargeable batteries on the market. As a machine learning specialist on the team responsible for building a machine learning model that can predict the chemical component interaction that maximizes battery capacity, you have decided that none of the built-in algorithms available in SageMaker fit your problem, as well as you would like. So you and your team have decided to create your own SageMaker algorithm resource. You’ll use this custom algorithm to train and run inferences on your model.
Which of the following steps do you NOT need to complete to create your custom algorithm for use in SageMaker?

Answer options:

A.Create Docker containers for your training and inference code.
B.Specify the hyperparameters that your algorithm supports.
C.Specify the metrics that your algorithm sends to CloudWatch when training.
D.The instance types your algorithm supports for training and inference.
E.Whether your algorithm supports distributed inference across multiple instances.

Answer: E
Option A is incorrect. SageMaker uses Docker containers for your custom algorithm training and hosting your algorithm. 
Option B is incorrect. When you create a custom algorithm resource in SageMaker, you need to specify the hyperparameters your algorithm will support.
Option C is incorrect. When you create a custom algorithm resource in SageMaker, you need to specify the metrics that your algorithm will send to CloudWatch when running your training jobs.
Option D is incorrect. When you create a custom algorithm resource in SageMaker, you need to specify the EC2 instance types your algorithm supports for training and inference.
Option E is correct. When you create a custom algorithm resource in SageMaker, you need to specify whether it supports distributed training across multiple instances, not distributed inference.
Reference:
Please see the Amazon SageMaker developer guide titled Use Your Own Algorithms or Models with Amazon SageMaker, and the Amazon SageMaker developer guide titled Create an Algorithm Resource.