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MLA-C01 Pack
Vendor: Amazon
Exam Code: MLA-C01
Exam Name: AWS Certified Machine Learning Engineer - Associate
Exam Questions: 85
Last Updated: December 8, 2025
Related Certifications: Amazon Associate
Exam Tags: Associate Level Machine Learning EngineersData Scientists
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Free Amazon MLA-C01 Exam Actual Questions

Question No. 1

An ML engineer is evaluating several ML models and must choose one model to use in production. The cost of false negative predictions by the models is much higher than the cost of false positive predictions.

Which metric finding should the ML engineer prioritize the MOST when choosing the model?

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Correct Answer: D

Question No. 2

A company has trained an ML model in Amazon SageMaker. The company needs to host the model to provide inferences in a production environment.

The model must be highly available and must respond with minimum latency. The size of each request will be between 1 KB and 3 MB. The model will receive unpredictable bursts of requests during the day. The inferences must adapt proportionally to the changes in demand.

How should the company deploy the model into production to meet these requirements?

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Correct Answer: A

Question No. 3

A company needs to create a central catalog for all the company's ML models. The models are in AWS accounts where the company developed the models initially. The models are hosted in Amazon Elastic Container Registry (Amazon ECR) repositories.

Which solution will meet these requirements?

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Correct Answer: C

Question No. 4

An ML engineer has developed a binary classification model outside of Amazon SageMaker. The ML engineer needs to make the model accessible to a SageMaker Canvas user for additional tuning.

The model artifacts are stored in an Amazon S3 bucket. The ML engineer and the Canvas user are part of the same SageMaker domain.

Which combination of requirements must be met so that the ML engineer can share the model with the Canvas user? (Choose two.)

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Correct Answer: B, C

Question No. 5

A company is planning to use Amazon Redshift ML in its primary AWS account. The source data is in an Amazon S3 bucket in a secondary account.

An ML engineer needs to set up an ML pipeline in the primary account to access the S3 bucket in the secondary account. The solution must not require public IPv4 addresses.

Which solution will meet these requirements?

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Correct Answer: D

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