AWS-CERTIFIED-MACHINE-LEARNING-ENGINEER-ASSOCIATE-MLA-C01Updated 8d ago · Aug 28, 2026

AWS Certified Machine Learning Engineer - Associate MLA-C01

271 questions across 1 topics, with suggested answers, explanations where available, and imported community discussion. The first 27 questions are free to preview.

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#	Topic	Questions	Free
1	Deployment and Orchestration of ML Workflows	271	27

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Question 1 · Deployment and Orchestration of ML Workflows Open

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?

Answer: C Amazon SageMaker Model Registry catalogs models in model groups, with each registered model package maintained as a version within its group. This provides built-in model version management without creating separate Amazon ECR repositories or manually maintaining version tags. **Learn more:** [Model Registry Models, Model Versions, and Model Groups - Amazon SageMaker AI](https://docs.aws.amazon.com/sagemaker/latest/dg/model-registry-models.html)

Question 2 · Deployment and Orchestration of ML Workflows Open

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 is experimenting with consecutive training jobs. How can the company MINIMIZE infrastructure startup times for these jobs?

Answer: B SageMaker managed warm pools retain provisioned training infrastructure after a training job finishes and reuse it for subsequent jobs with matching configurations. Reusing the retained infrastructure reduces the time spent provisioning resources, which minimizes startup latency for consecutive training jobs. **Learn more:** [SageMaker AI Managed Warm Pools](https://docs.aws.amazon.com/sagemaker/latest/dg/train-warm-pools.html)

Question 3 · ML Solution Monitoring, Maintenance, and Security Open

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 must implement a manual approval-based workflow to ensure that only approved models can be deployed to production endpoints. Which solution will meet this requirement?

Answer: D A SageMaker Pipelines workflow can register a model version with `PendingManualApproval`; after review, an authorized user updates its Model Registry approval status to `Approved` through the AWS SDK. Approved model versions can trigger CI/CD deployment, ensuring models are not deployed to production until approval is granted. **Learn more:** [Update the Approval Status of a Model - Amazon SageMaker AI](https://docs.aws.amazon.com/sagemaker/latest/dg/model-registry-approve.html) · [Deploy a Model from the Registry with Python - Amazon SageMaker AI](https://docs.aws.amazon.com/sagemaker/latest/dg/model-registry-deploy.html)