Q: 7
[Modeling]
A beauty supply store wants to understand some characteristics of visitors to the store. The store has
security video recordings from the past several years. The store wants to generate a report of hourly
visitors from the recordings. The report should group visitors by hair style and hair color.
Which solution will meet these requirements with the LEAST amount of effort?
Options
Discussion
C or A. If we strictly care about the least effort, semantic segmentation (C) should work best for isolating hair regions, then ResNet-50 is purpose-built for classes like hairstyle and color. But object detection (A) could get close if the hair region is obvious in most frames. I think C has the edge, but in a dataset with super clean frames, maybe A isn't as much extra work. Anyone disagree?
For me, C, semantic segmentation plus ResNet-50 lines up with least effort for visual grouping like this.
C , XGBoost is a trap here since it's not really for image stuff. Seen similar logic in practice questions too.
C imo
Nah, XGBoost is a common trap for image stuff. C here.
C imo, had something like this in a mock and ResNet-50 after semantic segmentation fit best for image tasks.
B , since XGBoost is great for classification. I realize it's usually for tabular data so maybe I'm missing something here, but it seems like with the right features from object detection this could work? Not 100% sure.
C, matches what I’ve seen in exam reports and AWS official guide samples for image-based tasks.
Likely C, since segmentation handles pixel-wise hair extraction for vision tasks and ResNet-50 works better for image classifications. XGBoost is more tabular.
C tbh, you can double check in the official guide or AWS practice exam for similar flows.
Be respectful. No spam.
