Azure AI-900 key terms

The field of building computer systems that perform tasks normally requiring human intelligence, such as understanding language, recognizing images, making recommendations, or supporting decisions.

A branch of AI where a model learns patterns from data instead of following hand-written rules. The trained model then makes predictions or decisions on new, unseen data.

A machine learning approach that uses artificial neural networks with many layers to learn complex patterns from large amounts of data, powering tasks such as image recognition and language models.

An AI workload that finds unusual items or events differing from the normal pattern in data, such as fraudulent transactions, failing equipment, or unusual sign-in activity.

A set of principles and practices for designing and deploying AI systems so they are fair, safe, private, inclusive, transparent, and accountable to the people affected by them.

A responsible AI principle requiring that an AI system treats all groups of people equitably and does not produce biased outcomes for people with similar relevant characteristics.

A responsible AI principle about designing AI systems so that people with different abilities, languages, and backgrounds can all use them and benefit from them.

A responsible AI principle requiring that AI systems operate consistently under normal and unexpected conditions, and that they are tested and controlled so they do not cause harm.

A responsible AI principle about protecting personal and organizational data used by AI systems, keeping it secure, and giving people control over how their information is used.

A responsible AI principle about making it understandable how an AI system works and why it produced a given result, so people can trust and challenge its output.

A responsible AI principle stating that the people and organizations who design and operate an AI system remain answerable for how it behaves and for the outcomes it causes.

A family of prebuilt cloud APIs, formerly called Cognitive Services, that add ready-made vision, speech, language, and decision capabilities to applications without training models from scratch.

A machine learning method where the training data includes both inputs and the correct answers, so the model learns to predict those answers for new inputs it has not seen.

A machine learning method that trains on data with no labels, letting the model discover structure on its own, such as natural groupings or unusual values in the data.

A machine learning method where an agent learns by acting in an environment and receiving rewards or penalties, gradually discovering the behavior that earns the highest total reward.

A supervised learning task that predicts a continuous numeric value, such as a price, temperature, or duration, rather than placing the item into a category.

A supervised learning task that predicts which category or class an item belongs to, such as spam versus not spam, or one of several product types.

An unsupervised learning task that groups similar items together based on their features, without being told in advance what the groups are or how many exist.

An individual measurable property of the data used as input to a machine learning model, such as age, price, or the pixel values of an image.

The known answer or target value attached to a training example in supervised learning, telling the model what the correct prediction should be.

A modeling problem where a model memorizes the training data too closely, including its noise, and therefore performs poorly on new, unseen data.

A feature of Azure Machine Learning that automatically tries many algorithms and settings, selects the best-performing model for your data, and can deploy it for you.

An AI workload in which systems interpret and analyze images and video to classify scenes, find objects, read text, or detect faces and movement.

A computer vision task that assigns one or more labels to a whole image, answering the question of what the image shows overall, for example cat or vehicle.

A computer vision task that identifies where objects are in an image by returning a bounding box and a class label for each detected object.

A computer vision task that classifies every pixel in an image into a category, producing a mask that shows the exact shape of each region, such as road or sky.

A computer vision capability that extracts printed or handwritten text from images and documents and converts it into machine-readable, searchable characters.

An AI workload that enables computers to understand, interpret, and generate human language in text or speech, covering tasks such as sentiment analysis, translation, and question answering.

A natural language processing task that evaluates text and reports whether the opinion expressed in a piece of text is positive, negative, neutral, or mixed.

A natural language processing task that finds and categorizes named things in text, such as people, organizations, locations, dates, and quantities.

A natural language processing capability, also called speech-to-text, that transcribes spoken audio into written text so it can be searched, analyzed, or acted on.

A natural language processing capability, also called text-to-speech, that converts written text into natural-sounding spoken audio in a chosen voice and language.

A natural language processing capability that converts text or speech from one language into another while preserving meaning, used to localize content and enable cross-language communication.

A natural language processing service that interprets a user's utterance to detect its intent and extract key entities, so an application or bot can respond appropriately.

A category of AI that creates new content, such as text, images, code, or audio, by learning patterns from large amounts of existing data rather than only classifying or predicting.

A very large neural network trained on huge amounts of text so it can predict and generate language, supporting tasks such as summarization, question answering, and conversation.

The input text or instruction a person gives to a generative AI model, which guides what the model produces; clearer, more specific prompts generally yield better results.

A technique that first retrieves relevant documents or data from a trusted source and then gives them to a generative model, so answers are grounded in real, current information.

A Microsoft Azure service that provides access to OpenAI's generative models, with Azure security, compliance, private networking, and content filtering built in so organizations can use them safely.

A failure of a generative AI model where it produces content that sounds plausible but is factually wrong or invented, such as citing a source that does not exist.