Free Amazon MLS-C01 Actual Exam Questions - Question 11 Discussion
Question No. 11
[Data Engineering]
A manufacturing company uses machine learning (ML) models to detect quality issues. The models
use images that are taken of the company's product at the end of each production step. The company
has thousands of machines at the production site that generate one image per second on average.
The company ran a successful pilot with a single manufacturing machine. For the pilot, ML specialists
used an industrial PC that ran AWS IoT Greengrass with a long-running AWS Lambda function that
uploaded the images to Amazon S3. The uploaded images invoked a Lambda function that was
written in Python to perform inference by using an Amazon SageMaker endpoint that ran a custom
model. The inference results were forwarded back to a web service that was hosted at the
production site to prevent faulty products from being shipped.
The company scaled the solution out to all manufacturing machines by installing similarly configured
industrial PCs on each production machine. However, latency for predictions increased beyond
acceptable limits. Analysis shows that the internet connection is at its capacity limit.
How can the company resolve this issue MOST cost-effectively?
A manufacturing company uses machine learning (ML) models to detect quality issues. The models
use images that are taken of the company's product at the end of each production step. The company
has thousands of machines at the production site that generate one image per second on average.
The company ran a successful pilot with a single manufacturing machine. For the pilot, ML specialists
used an industrial PC that ran AWS IoT Greengrass with a long-running AWS Lambda function that
uploaded the images to Amazon S3. The uploaded images invoked a Lambda function that was
written in Python to perform inference by using an Amazon SageMaker endpoint that ran a custom
model. The inference results were forwarded back to a web service that was hosted at the
production site to prevent faulty products from being shipped.
The company scaled the solution out to all manufacturing machines by installing similarly configured
industrial PCs on each production machine. However, latency for predictions increased beyond
acceptable limits. Analysis shows that the internet connection is at its capacity limit.
How can the company resolve this issue MOST cost-effectively?
Select one option, then reveal solution.
US
SK
Shoaib K.
2026-02-10
D/B? D reduces internet traffic, but if local PCs struggle with the model, compressing images (B) might ease bandwidth without heavy hardware upgrade. B feels like a simpler fix before full edge deployment.
0
JS
James S.
2026-01-23
Probably D, moving inference to edge cuts internet load and latency issues completely.
0
CJ
Chris J.
2026-01-16
Option D, moving inference to the edge avoids internet bottlenecks completely.
0