AWS Certified Machine Learning Engineer – Associate (MLA-C01) — Question 137

A company has several teams that have developed separate prediction models on their own laptops. The teams developed the models by using Python with scikit-learn and TensorFlow frameworks.

The company must rebuild the models and must integrate the models into an ML infrastructure that the company manages by using Amazon SageMaker. The company also must incorporate the models into a model registry.

Which solution will meet these requirements with the LEAST operational overhead?

Answer options

• A. Export the models from the laptops to an Amazon S3 bucket. Use an Amazon API Gateway REST API and AWS Lambda functions with SageMaker endpoints to access the models. Register the models in the SageMaker Model Registry.
• B. Import the models into the SageMaker Model Registry. Use SageMaker to run the imported models.
• C. Use code from the laptops to create containers for the models. Use the bring your own container (BYOC) functionality of SageMaker to import and use the models. Register the models in the SageMaker Model Registry.
• D. Import the Python-based models into SageMaker. Rebuild the scikit-learn and TensorFlow models in SageMaker. Register all the models in the SageMaker Model Registry.

Correct answer: D

Explanation

The correct answer is D because it ensures that the models are built natively in SageMaker, allowing for optimal integration with its features and minimizing operational overhead. Options A and C introduce additional complexity by requiring external services or containerization, while option B does not address the need to rebuild the models using SageMaker's capabilities.