AWS Certified Machine Learning Engineer – Associate (MLA-C01) — Question 190
A company has trained an ML model that is packaged in a container. The company will integrate the model with an existing Python web application. The company needs to host the model on AWS by using Kubernetes.
The company does not want to manage the control plane and must provision the resources in a repeatable manner. The infrastructure must be provisioned by using Python.
Which solution will meet these requirements?
Answer options
- A. Use AWS CloudFormation to provision Amazon EC2 instances in multiple Availability Zones. Set up a Kubernetes cluster. Host the model container on the Kubernetes cluster.
- B. Use the AWS CLI to provision an Amazon Elastic Kubernetes Service (Amazon EKS) cluster. Store the image in an Amazon Elastic Container Registry (Amazon ECR) repository. Host the model container on the EKS cluster.
- C. Use the AWS Cloud Development Kit (AWS CDK) to provision an Amazon Elastic Kubernetes Service (Amazon EKS) cluster. Store the image in an Amazon Elastic Container Registry (Amazon ECR) repository. Host the model container on the EKS cluster.
- D. Use AWS CloudFormation to provision an Amazon Elastic Kubernetes Service (Amazon EKS) cluster. Store the image in an Amazon Elastic Container Registry (Amazon ECR) repository. Host the model container on the EKS cluster.
Correct answer: C
Explanation
The correct answer is C because the AWS Cloud Development Kit (AWS CDK) allows for infrastructure to be defined using Python, fulfilling the requirement for repeatable resource provisioning. Option A doesn't use the preferred method (AWS CDK), while B uses the AWS CLI and D uses CloudFormation, neither of which are Python-based solutions for provisioning in this scenario.