AWS Certified Machine Learning Engineer – Associate (MLA-C01) — Question 198

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?

Answer options

• A. Store the images in an Amazon S3 bucket. Deploy the model on SageMaker AI. Use batch transform jobs for model inference. Use an Amazon Simple Queue Service (Amazon SQS) queue to notify users.
• B. Store the images in an Amazon S3 bucket. Deploy the model on SageMaker AI. Use an asynchronous inference strategy for model inference. Use an Amazon Simple Notification Service (Amazon SNS) topic to notify users.
• C. Store the images in an Amazon Elastic File System (Amazon EFS) file system. Deploy the model on SageMaker AI. Use batch transform jobs for model inference. Use an Amazon Simple Queue Service (Amazon SQS) queue to notify users.
• D. Store the images in an Amazon Elastic File System (Amazon EFS) file system. Deploy the model on SageMaker AI. Use an asynchronous inference strategy for model inference. Use an Amazon Simple Notification Service (Amazon SNS) topic to notify users.

Correct answer: B

Explanation

Option B is correct because it utilizes an asynchronous inference strategy, which is suitable for processing high-resolution images and notifying users via Amazon SNS when the task is complete. Options A and C use batch transform jobs, which are not ideal for real-time user notifications. Option D, while it uses an asynchronous strategy, employs Amazon EFS instead of S3, which is unnecessary for this case.