MLA-C01 · Question #216
A company is developing an internal cost-estimation tool that uses an ML model in Amazon SageMaker AI. Users upload high-resolution images to the tool. The model must process each image and predict th
The correct answer is B. Store the images in an Amazon S3 bucket. Deploy the model on SageMaker AI. Use an. SageMaker asynchronous inference is designed for large payloads and longer processing times by decoupling request submission from response delivery. Storing the images in Amazon S3 fits the standard async inference input/output pattern, and using Amazon SNS provides a simple push
Question
A company is developing an internal cost-estimation tool that uses an ML model in Amazon SageMaker AI. Users upload high-resolution images to the tool. The model must process each image and predict the cost of the object in the image. The model also must notify the user when processing is complete. Which solution will meet these requirements?
Options
- AStore the images in an Amazon S3 bucket. Deploy the model on SageMaker AI. Use batch
- BStore the images in an Amazon S3 bucket. Deploy the model on SageMaker AI. Use an
- CStore the images in an Amazon Elastic File System (Amazon EFS) file system. Deploy the model
- DStore the images in an Amazon Elastic File System (Amazon EFS) file system. Deploy the model
How the community answered
(20 responses)- A5% (1)
- B80% (16)
- C5% (1)
- D10% (2)
Explanation
SageMaker asynchronous inference is designed for large payloads and longer processing times by decoupling request submission from response delivery. Storing the images in Amazon S3 fits the standard async inference input/output pattern, and using Amazon SNS provides a simple push-notification mechanism to alert users when processing completes.
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