You work as a machine learning specialist for a research data streaming service that serves research reference content to subscribers. Your company’s subscriber base is primarily made up of university research staff. However, your company occasionally produces research content that has broader appeal, and your service gets very big spikes in requests for streaming traffic. Your machine learning team has a critical component in the content delivery process. You have a recommendation engine model variant that processes inference requests for every content streaming request. When your model variant receives these spikes in inference requests, your company’s streaming service suffers poor performance. You have decided to use SageMaker autoscaling to meet the varying demand for your model variant inference requests. Which type of scaling policy should you use in your SageMaker autoscaling implementation?

Answer options:

A.target-tracking scaling
B.step scaling
C.simple scaling
D.scheduled scaling

Correct Answer: A
Option A is correct. AWS recommends that you use scaling policies for your autoscaling configuration because it is fully automated.
Option B is incorrect. AWS recommends that you use step scaling when you need an advanced configuration, such as specifying how many instances to deploy under certain circumstances. You don’t have a specialized need like this, so you should use target-tracking scaling.
Option C is incorrect. SageMaker autoscaling doesn’t have a simple scaling policy.
Option D is incorrect. SageMaker autoscaling doesn’t have a scheduled scaling policy.
References:
Please see the Amazon SageMaker developer guide titled Automatically Scale Amazon SageMaker Models (https://docs.aws.amazon.com/sagemaker/latest/dg/endpoint-auto-scaling.html),
The Amazon SageMaker developer guide titled Prerequisites (https://docs.aws.amazon.com/sagemaker/latest/dg/endpoint-auto-scaling-prerequisites.html)