AI Domains

AI can support healthcare through analysis and assistance, farming through crop or soil monitoring, and education through adaptive learning or feedback. Therefore all three listed areas are valid applications, making option D the complete answer. Selecting only one area would ignore the other legitimate uses described by the question.

A face-recognition system analyses visual features and patterns in an image or video and compares them with stored information to identify or verify a person. This data-based recognition is an application of Artificial Intelligence, often involving computer vision. A list, paper file, or simple lock cannot perform that analysis.

A voice assistant uses AI-related technologies to recognise speech, interpret language, determine the user’s intent, and produce an answer or action. A battery supplies energy and wires may provide connections, but neither understands spoken language. An ordinary fan has no such language-processing capability.

A recommendation system analyses information such as preferences, previous choices, searches, or behaviour to suggest content or products that may be relevant to a user. It supports personalisation, although its suggestions are not guaranteed to be perfect. Shutting down a computer, painting, and counting papers are unrelated functions.

Identifying an object in an image requires analysing visual information such as shapes, colours, edges, and patterns. This is visual recognition and is commonly associated with computer vision in AI. Numerical calculation processes numbers, while typing and printing are input or output activities rather than image-understanding abilities.

AI can analyse information about weather, soil, crop condition, and past yields to support decisions about sowing, irrigation, pest management, or crop care. Such advice should still be checked by farmers because conditions can change. The other options do not describe a farming-related AI application.

An AI translation system analyses words, sentence structure, and often context to produce an equivalent or meaningful expression in another language. It is not simply sorting words alphabetically. Converting handwriting to print is recognition or digitisation, and identifying objects in images is computer vision, not language translation.

The app in option A uses a person’s viewing history as data, identifies patterns in interests, and ranks or suggests content likely to be relevant. Because suggestions can differ among users, it is personalised. A fixed list is not personalised, and an alarm or printed timetable does not learn preferences or generate recommendations.

A voice assistant first processes spoken sound, converts it into words, interprets the meaning of the request, and then produces a suitable reply or action. The key AI ability described is language understanding, often supported by speech recognition and natural-language processing. Counting paper, painting, or opening locks is not shown by this example.

A face-recognition system analyses visual input from a camera, extracts features such as shape and relative arrangement, and compares them with learned patterns to identify or verify a face. This is visual recognition, a computer-vision task. Sound amplification changes volume, while printing and decoration do not interpret image content.

In healthcare, AI can analyse medical images, laboratory results, symptoms, or other clinical data to identify patterns that may suggest disease. It supports trained professionals rather than replacing medical judgement, and results require careful validation. The other options are routine physical activities unrelated to disease detection.

A translation system processes words or speech in one language and expresses their meaning in another language. That is why option A is correct. Option B is speech recognition or transcription, where spoken language is changed into written text, usually in the same language. Image recognition and weighing are different tasks.

Classification means assigning items to categories according to their features or patterns. A spam filter examines characteristics such as words, sender details, or message patterns and places each email in a category such as spam or not spam. Option A therefore represents classification; deleting, sitting, or dismantling does not assign categories.

A self-driving vehicle can use cameras, sensors, and AI models to detect lanes, roads, vehicles, pedestrians, and obstacles. It then uses the detected information to support actions such as steering, slowing, or stopping. Option A is therefore a transport application. The other activities are ordinary manual actions and do not involve AI-based sensing or decision support.

AI can assist trained healthcare professionals by highlighting possible patterns in medical images, reports, or records. It is a support tool, not an automatic replacement for clinical examination, professional judgement, or patient privacy. Option A describes appropriate assistance. The other options involve disclosure, blind reliance, or unsafe treatment and are not responsible uses.

Language understanding involves processing words and sentences to identify meaning, context, or intention. Understanding a written sentence is therefore a Natural Language Processing task and matches option A. The other options involve physical objects and changes in the environment; they do not require interpreting human language or extracting meaning from text.

A recommendation system analyses information such as previous choices, interests, learning activity, or ratings and uses patterns to suggest potentially useful content. Option A describes personalised study recommendations. Sending identical material is not personalised, alphabetical sorting follows a simple ordering rule, and manual scheduling does not demonstrate an AI recommendation process.

Text summarisation requires a system to process sentences, identify important ideas, and express them in a shorter form. These are language-related operations commonly associated with Natural Language Processing. Option A is therefore correct. Painting, heating, and bending are physical activities and do not require understanding the meaning or structure of human language.

Classification assigns an item to one of defined categories, while pattern recognition helps identify the features that support that assignment. For example, an AI system may use patterns in a message to classify it as spam or not spam. Option A correctly names both ideas. Random arrangement and deletion do not identify meaningful categories.

A route-recommendation system can analyse traffic information, compare possible roads, and suggest an option that may save time or avoid congestion. The final choice can still belong to the user, so this is decision support rather than necessarily an autonomous decision. Painting, storing shoes, and closing a book do not use data analysis to recommend an option.

The input in this example is an image, and the system analyses visual features such as shape, colour, text, and symbols to identify the road sign. This is visual recognition, commonly associated with computer vision. Language translation works between languages, speech recognition processes audio, and numerical calculation operates on numbers rather than primarily identifying an image.

An AI translation system analyses words, sentences, and sometimes context in a source language, then produces an equivalent or near-equivalent meaning in another language. It may help with vocabulary, grammar, and sentence patterns, although translations can still require human review for accuracy and context. The other choices are physical tasks unrelated to language conversion.

Pattern recognition is the process of finding repeated features, relationships, sequences, or similarities in data. An AI system may use these patterns to classify an image, identify a speech command, or make a prediction. Deleting information removes data, answering without input lacks a basis, and decorating a machine changes its appearance; none of these is pattern recognition.

Speech recognition receives spoken audio, analyses sound patterns, and converts them into identified words, commands, or a transcript. A microphone may capture the sound, but the relevant input is the speech signal. Written-text character recognition is associated with OCR, image analysis processes visual input, and distance measurements are numerical or sensor data rather than spoken language.

Identifying a tree in an image requires analysing visual features such as shapes, colours, edges, and textures in the image data. This is a computer-vision task. Lifting a chair, folding paper, or ringing a bell describes a physical action; such an action might be automated by a robot, but it does not by itself show AI interpreting visual information.