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AWS
MLA-C01
AWS Certified Machine Learning Engineer - Associate MLA-C01
08-27
AWS · CURRENT EDITION
MLA-C01EXAM PAPER PDF
AWS Certified Machine Learning Engineer - Associate MLA-C01
MLA-C01 is the associate-level AWS credential for practitioners who implement and operationalize machine learning workloads. It covers data preparation, model development, deployment, orchestration, monitoring, security, and maintenance of ML solutions on AWS. Use the official AWS exam guide to confirm the current domains and in-scope services.
| QUESTIONS | 271 questions · answers and explanations included |
|---|---|
| PUBLISHED | 2026-08-27 |
| LANGUAGE | English questions · localized guide text |
| ACCESS | 30 days · updated editions |
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Six questions are open without an account or card, in the same reading order as the PDF.
START THE FREE SAMPLE →Case Study - A company is building a web-based AI application by using Amazon SageMaker. The application will provide the following capabilities and features: ML experimentation, training, a central model registry, model deployment, and model monitoring. The application must ensure secure and isolated use of training data during the ML lifecycle. The training data is stored in Amazon S3. The company needs to use the central model registry to manage different versions of models in the application. Which action will meet this requirement with the LEAST operational overhead?
ACreate a separate Amazon Elastic Container Registry (Amazon ECR) repository for each model.
BUse Amazon Elastic Container Registry (Amazon ECR) and unique tags for each model version.
CUse the SageMaker Model Registry and model groups to catalog the models.
DUse the SageMaker Model Registry and unique tags for each model version.
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