Which deployment compute option should you use?

You develop and train a machine learning model to predict fraudulent transactions for a hotel booking website.

Traffic to the site varies considerably. The site experiences heavy traffic on Monday and Friday and much lower traffic on other days. Holidays are also high web traffic days. You need to deploy the model as an Azure Machine Learning real-time web service endpoint on compute that can dynamically scale up and down to support demand.

Which deployment compute option should you use?
A . attached Azure Databricks cluster
B . Azure Container Instance (ACI)
C . Azure Kubernetes Service (AKS) inference cluster
D . Azure Machine Learning Compute Instance
E . attached virtual machine in a different region

View Answer

Answer: D

Explanation:

Azure Machine Learning compute cluster is a managed-compute infrastructure that allows you to easily create a single or multi-node compute. The compute is created within your workspace region as a resource that can be shared with other users in your workspace. The compute scales up automatically when a job is submitted, and can be put in an Azure Virtual Network.

Reference: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-create-attach-compute-sdk