Explanation:

In the Microsoft Azure AI Fundamentals (AI-900) curriculum, computer vision workloads are grouped into distinct types, each serving a specific purpose. The three major workloads illustrated here are image classification, object detection, and optical character recognition (OCR). Understanding their use cases is essential for correctly mapping them to real-world scenarios.
* Generate captions for images # Image classificationThe image classification workload is used to identify the main subject or context of an image and assign descriptive labels. In Microsoft Learn's
"Describe features of computer vision workloads," image classification models are trained to recognize content (e.g., a cat, a beach, or a city). Caption generation expands on classification results by describing the image's contents in human-readable language-based on what the model identifies as key visual features.
* Extract movie title names from movie poster images # Optical character recognition (OCR)OCR is a vision workload that detects and extracts text from images. Azure AI Vision's Read API or Document Intelligence OCR models can identify printed or handwritten text within posters, signs, or documents.
In this case, the movie title text from a poster is best extracted using OCR.
* Locate vehicles in images # Object detectionThe object detection workload identifies multiple objects within an image and provides their locations using bounding boxes. It's ideal for tasks like counting cars in a parking lot or tracking objects in traffic images.