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A company has an online learning platform that teaches data science.

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A company has an online learning platform that teaches data science. The platform uses the AWS Cloud to provision on-demand lab environments for its students. Each student receives a dedicated AWS account for a short time. Students need access to ml.p2.xlarge instances to run a single Amazon SageMaker machine learning training job and to deploy the inference endpoint. Account provisioning is automated. The accounts are members of an organization in AWS Organizations with all features enabled. The accounts must be provisioned in the ap-southeast-2 Region. The default resource usage quotas are not sufficient for the accounts. A solutions architect must enhance the account provisioning process to include automated quota increases. Which solution will meet these requirements?

A.

Create a quota request template in the us-east-1 Region in the organization's management account. Enable template association. Add a quota for SageMaker in ap-southeast-2 for ml.p2.xlarge training job usage. Set the desired quota to 1. Add a quota for SageMaker in ap-southeast-2 for ml.p2.xlarge endpoint usage. Set the desired quota to 1.

B.

Create a quota request template in the us-east-1 Region in the organization's management account. Enable template association. Add a quota for SageMaker in ap-southeast-2 for ml.p2.xlarge training warm pool usage. Set the desired quota to 2.

C.

Create a quota request template in ap-southeast-2 in the organization's management account. Enable template association. Add a quota for SageMaker in the us-east-1 Region for ml.p2.xlarge training job usage. Set the desired quota to 1. Add a quota for SageMaker in us-east-1 for ml.p2.xlarge endpoint usage. Set the desired quota to 1.

D.

Create a quota request template in ap-southeast-2 in the organization's management account. Enable template association. Add a quota for SageMaker in the us-east-1 Region for ml.p2.xlarge training warm pool usage. Set the desired quota to 2.

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