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Hello Tanner,
Thanks for taking the time to share your feedback on this question. We completely see your point and value your input.
The exam answer for this question is Option 2, which uses Amazon SageMaker Asynchronous Inference with an automatic scaling policy. As explained in the question, the workload involves high-resolution images that can reach up to 60 MB and may experience unpredictable traffic spikes during global promotional campaigns. SageMaker Asynchronous Inference is designed for large payloads and long-running inference requests, and it can queue incoming requests while automatically scaling endpoint capacity based on demand. Because of that, the exam rationale focuses on scalable, managed asynchronous ML inference with minimal infrastructure management.
Option 4, which uses Amazon SQS and AWS Lambda, is not the best answer in this case because Lambda is not ideal for large, resource-intensive ML inference workflows. While SQS and Lambda are effective for event-driven processing, Lambda has execution time, payload, memory, and runtime limits that can make it less suitable for handling large image-processing workloads at scale. The question is looking for a managed inference solution that can process large image payloads and handle fluctuating demand more directly.
That said, we completely understand your observation. The scenario mentions Amazon Rekognition and Amazon Bedrock, which can make Option 4 appear more aligned with the described workflow. To avoid confusion, we’ll update the scenario to better reflect the accurate terms and make it clearer that the question is focused on SageMaker Asynchronous Inference for large-scale image inference workloads.
Thank you for the insightful feedback. This will help us improve the quality and clarity of our materials! Feel free to reach out if you have any follow-up questions.
Regards,
Lois @ Tutorials Dojo
