AWS Certified Cloud Practitioner
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A logistics company is developing a delivery-route optimization AI that improves decisions by learning from previous deliveries — rewarding faster routes and penalizing delays. Which learning approach best fits this problem?
Real Exam
Community
RRitesh
0
A
Unsupervised learning
B
Self-supervised learning
C
Reinforcement learning
D
Transfer learning
Explanation:
Explanation
This scenario describes reinforcement learning because:
- Reward/Penalty System: The AI receives rewards for faster routes (positive reinforcement) and penalties for delays (negative reinforcement)
- Learning from Experience: The system improves by learning from previous delivery outcomes
- Decision Optimization: The goal is to optimize route decisions based on feedback
Why other options are incorrect:
- A) Unsupervised learning: Used for finding patterns in unlabeled data, not for reward-based optimization
- B) Self-supervised learning: Involves creating labels from the data itself, not reward-based decision making
- D) Transfer learning: Focuses on applying knowledge from one domain to another, not reward-based optimization
Reinforcement learning is specifically designed for scenarios where an agent learns to make decisions by interacting with an environment and receiving rewards or penalties for its actions.
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