You work for a logistics company that specializes in the storage, movement, and control of massive amounts of packages. You are on the machine learning team assigned the task of building a machine learning model to assist in the control of your company’s package logistics. Specifically, your model needs to predict the routes your package movers should take for optimal delivery and resource usage. The model requires various transformations to be performed on the data. You also want to get inferences on entire datasets once you have your model in production. Additionally, you won’t need a persistent endpoint for applications to call to get inferences.
Which type of production deployment would you use to get predictions from your model in the most expeditious manner?

Answer options:

A.SageMaker Hosting Services
B.SageMaker Batch Transform
C.SageMaker Containers
D.SageMaker Elastic Inference

Answer: B
Option A is incorrect. SageMaker Hosting Services is used for applications to send requests to an HTTPS endpoint to get inferences. This type of deployment is used when you need a persistent endpoint for applications to call to get inferences.
Option B is correct. SageMaker Batch Transform is used to get inferences for an entire dataset, and you don’t need a persistent endpoint for applications to call to get inferences.
Option C is incorrect. SageMaker Containers is a service you can use to create your own Docker containers to deploy your models. This would not be the most expeditious option.
Option D is incorrect. SageMaker Elastic Interface is used to accelerate deep learning inference workloads. This service alone would not give you the batch transform capabilities you need. 
Reference:
Please see the Amazon SageMaker developer guide titled Deploy a Model on Amazon SageMaker Hosting Services, the Amazon SageMaker developer guide titled Get Inferences for an Entire Dataset with Batch Transform, the Amazon Elastic Inference developer guide titled What Is Amazon Elastic Inference?, and the Amazon SageMaker developer guide titled Amazon SageMaker Containers: a Library to Create Docker Containers.