Amazon Exam MLA-C01 Topic 1 Question 12 Discussion

Actual exam question for Amazon's MLA-C01 exam

Question #: 12
Topic #: 1

An ML engineer needs to implement a solution to host a trained ML model. The rate of requests to the model will be inconsistent throughout the day.

The ML engineer needs a scalable solution that minimizes costs when the model is not in use. The solution also must maintain the model's capacity to respond to requests during times of peak usage.

Which solution will meet these requirements?

ACreate AWS Lambda functions that have fixed concurrency to host the model. Configure the Lambda functions to automatically scale based on the number of requests to the model.

BDeploy the model on an Amazon Elastic Container Service (Amazon ECS) cluster that uses AWS Fargate. Set a static number of tasks to handle requests during times of peak usage.

CDeploy the model to an Amazon SageMaker endpoint. Deploy multiple copies of the model to the endpoint. Create an Application Load Balancer to route traffic between the different copies of the model at the endpoint.

DDeploy the model to an Amazon SageMaker endpoint. Create SageMaker endpoint auto scaling policies that are based on Amazon CloudWatch metrics to adjust the number of instances dynamically.

Show Suggested Answer

Suggested Answer: D

by Rosenda at Nov 01, 2025, 07:53 PM

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Gwen

2 hours ago

I'm leaning towards option D as well. The ability to dynamically adjust the number of instances based on usage patterns seems like it would meet the requirements of scalability and cost-effectiveness.

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Sarah

6 days ago

Option C with the Application Load Balancer routing traffic between multiple copies of the model on a SageMaker endpoint also seems like a good choice. That way you can handle fluctuations in demand without over-provisioning resources.

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Tonette

11 days ago

I'm a bit confused by the different options. Can someone explain the differences between the AWS Lambda and Amazon ECS solutions? I'm not sure I fully understand how they would work for this use case.

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Craig

17 days ago

I think option D sounds like the best approach here. Deploying the model to a SageMaker endpoint and using auto-scaling policies based on CloudWatch metrics seems like a scalable and cost-effective solution.

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