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Answer: Use Cloud Vision AutoML with the existing dataset.
**Explanation:** Option A is the best choice for this Proof-of-Concept scenario: - **Time constraint** - "Within a few working days" requirement makes AutoML ideal - **Dataset size** - 750 classes × 1000 examples = 750,000 images - sufficient for AutoML - **No data reduction needed** - Option B would reduce model accuracy unnecessarily - **Custom model requirement** - Standard Vision API (option C) doesn't support custom component recognition - **Quick deployment** - AutoML provides trained model without ML expertise - **Transfer learning** - Option D would take significantly longer and require ML expertise Cloud Vision AutoML: - Automates the entire ML pipeline - Handles custom image classification - Provides REST API for easy integration - Suitable for the 750-class classification problem - Meets the rapid POC timeline requirement
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NO.24 You work for a manufacturing company that sources up to 750 different components, each from a different supplier. You've collected a labeled dataset that has on average 1000 examples for each unique component. Your team wants to implement an app to help warehouse workers recognize incoming components based on a photo of the component. You want to implement the first working version of this app (as Proof-Of-Concept) within a few working days. What should you do?
A
Use Cloud Vision AutoML with the existing dataset.
B
Use Cloud Vision AutoML, but reduce your dataset twice.
C
Use Cloud Vision API by providing custom labels as recognition hints.
D
Train your own image recognition model leveraging transfer learning techniques.
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