Explanation:

According to the Microsoft Azure AI Fundamentals (AI-900) Official Study Guide and the Microsoft Learn module "Explore computer vision in Microsoft Azure," there are multiple types of computer vision tasks, each designed for different goals such as recognizing, categorizing, or locating objects within an image.
In this question, the application performs two distinct tasks: locating birds within an image and identifying their species. Each of these corresponds to a different type of computer vision capability.
* Locate the birds # Object detection
* Object detection is used when an AI system needs to identify and locate multiple objects within a single image.
* It not only recognizes what the object is but also provides bounding boxes that indicate the exact position of each object.
* In this scenario, locating the birds (drawing rectangles around each bird) is achieved through object detection models, such as those available in the Azure Custom Vision Object Detection domain.
* Identify the species of the birds # Image classification
* Image classification focuses on identifying what is in the image rather than where it is.
* It assigns a single label (or multiple labels in multilabel classification) to an entire image based on its contents.
* In this case, determining the species of a bird (e.g., robin, eagle, parrot) is achieved through image classification, where the model compares visual features against learned patterns from training data.
Incorrect options:
* Automated captioning generates descriptive sentences about an image, not object locations or classifications.
* Optical character recognition (OCR) extracts text from images, irrelevant in this case.

Within Azure AI Document Intelligence (formerly Form Recognizer), the Contract prebuilt model is designed to extract key information from legal and business contracts, including parties, jurisdictions, dates, and terms.
According to Microsoft Learn, this prebuilt model identifies structured entities such as contracting parties, effective dates, governing jurisdictions, and termination clauses.
* Layout (B) extracts text, tables, and structure but does not identify semantic information such as parties or jurisdictions.
* General document (C) extracts key-value pairs and entities but lacks domain-specific contract analysis.
* Read (D) performs OCR (optical character recognition) to extract raw text but not contextual metadata.
Thus, when the requirement is to extract parties and jurisdictions from a legal document, the Contract model is the correct Azure AI Document Intelligence choice.

B: Azure's Computer Vision service provides developers with access to advanced algorithms that process images and return information based on the visual features you're interested in. For example, Computer Vision can determine whether an image contains adult content, find specific brands or objects, or find human faces.
C: Computer Vision includes Optical Character Recognition (OCR) capabilities. You can use the new Read API to extract printed and handwritten text from images and documents.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/home Detect faces in an image - Face API Microsoft Azure provides multiple cognitive services that you can use to detect and analyze faces, including:
Computer Vision, which offers face detection and some basic face analysis, such as determining age.
Video Indexer, which you can use to detect and identify faces in a video.
Face, which offers pre-built algorithms that can detect, recognize, and analyze faces.
Recognize hand written text - Read API
The Read API is a better option for scanned documents that have a lot of text. The Read API also has the ability to automatically determine the proper recognition model

Object detection is similar to tagging, but the API returns the bounding box coordinates for each tag applied.
For example, if an image contains a dog, cat and person, the Detect operation will list those objects together with their coordinates in the image.
Reference:
https://docs.microsoft.com/en-us/ai-builder/object-detection-overview
https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/overview-ocr
https://docs.microsoft.com/en-us/azure/azure-video-analyzer/video-analyzer-for-media-docs/video-indexer- overview

Explanation:

According to the Microsoft Azure AI Fundamentals (AI-900) study material and Azure OpenAI Service documentation, large language models (LLMs) such as GPT are capable of performing multiple natural language processing (NLP) tasks depending on the intent of the prompt. These tasks generally fall into categories like classification, generation, summarization, and translation, each with a distinct purpose and output type.
* Classifying - This process involves analyzing text and assigning it to a predefined category or label based on its content. The scenario "Detect the genre of a work of fiction" clearly fits this category. The model must evaluate the text and determine whether it belongs to genres like mystery, romance, or science fiction. This is a classic text classification problem, as the output is a discrete category derived from textual features.
* Summarizing - This process means condensing lengthy text into a shorter version that preserves the key information. In the scenario "Create a list of bullet points based on text input," the model extracts essential information and reformats it as concise bullet points, which is an abstraction form of summarization. Summarization models help users quickly understand the main ideas from long documents, meeting efficiency and readability goals.
* Generating - This refers to the LLM's ability to produce new, creative content based on input instructions. The task "Create advertising slogans from a product description" represents generation because it requires the model to construct original text that didn't previously exist. Generation tasks showcase the creativity and contextual fluency of models like GPT in marketing and content creation.
Thus, these mappings align directly with the Azure OpenAI LLM capabilities taught in AI-900, linking each NLP process with its most suitable real-world task.