Amazon
MLA-C01 · Question #112
A company runs Amazon SageMaker ML models that use accelerated instances. The models require real-time responses. Each model has different scaling requirements. The company must not allow a cold start
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Deployment and Orchestration of ML Workflows
Question
A company runs Amazon SageMaker ML models that use accelerated instances. The models require real-time responses. Each model has different scaling requirements. The company must not allow a cold start for the models. Which solution will meet these requirements?
Options
- ACreate a SageMaker Serverless Inference endpoint for each model. Use provisioned concurrency
- BCreate a SageMaker Asynchronous Inference endpoint for each model. Create an auto scaling
- CCreate a SageMaker endpoint. Create an inference component for each model. In the inference
- DCreate an Amazon S3 bucket. Store all the model artifacts in the S3 bucket. Create a SageMaker
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Topics
#SageMaker Inference Components#Real-time Inference#No Cold Start#ML Model Deployment