Microsoft Azure AI Fundamentals (AI-900) Practice Exam

If a company wants to build a recycling machine that identifies specific bottle shapes, which AI workload should they use?

1. Natural language processing

2. Computer vision

3. Reinforcement learning

4. Predictive analytics

The correct answer is: Computer vision

The most suitable workload for building a recycling machine that identifies specific bottle shapes is computer vision. This AI technique is specifically designed to interpret and analyze visual data from the world, such as images and videos. In the context of identifying bottle shapes, a computer vision system can be trained on images of different bottle types, using labeled datasets to recognize and distinguish between various shapes and features. Computer vision utilizes techniques such as image classification, object detection, and image segmentation to enable machines to "see" and understand visual inputs. For a recycling machine, leveraging computer vision would allow it to accurately identify the bottle shapes it processes, ensuring effective sorting and recycling operations. On the other hand, natural language processing focuses on understanding and analyzing human language, which does not pertain to identifying visual objects. Reinforcement learning involves training algorithms through rewards and penalties for decision-making tasks, which is not directly applicable in this context of visual identification. Predictive analytics is concerned with forecasting future outcomes based on historical data but does not provide the visual recognition capabilities required to identify shapes. Thus, computer vision is the appropriate choice for this scenario.