Azure AI-900 flashcards

32 cards. Read the term and the definition here. The deck at the top of this page is optional.

Fairness

A Responsible AI principle requiring that AI systems treat all groups of people equitably and avoid bias that favors or disadvantages particular populations.

Reliability and safety

A Responsible AI principle stating that AI systems must operate consistently, handle unexpected conditions, and avoid causing harm under normal and adverse circumstances.

Transparency

A Responsible AI principle requiring that people understand how an AI system works, what data it uses, and how it reaches its decisions.

Accountability

A Responsible AI principle meaning humans remain responsible for AI systems and their outcomes, with governance and oversight built into the design.

Inclusiveness

A Responsible AI principle requiring AI systems to empower and benefit everyone, including people with disabilities and diverse backgrounds, rather than exclude them.

Privacy and security

A Responsible AI principle requiring that data used by AI systems is protected, handled lawfully, and secured against unauthorized access throughout the system lifecycle.

Knowledge mining

An AI workload that uses cognitive skills such as OCR, entity recognition, and key phrase extraction to extract insights from large volumes of unstructured documents.

Supervised learning

A machine learning approach that trains a model on labeled data, where each input example is paired with the known correct output called a label.

Unsupervised learning

A machine learning approach that trains on unlabeled data to discover patterns or groupings, such as clusters, without any predefined correct answers.

Regression

A supervised learning task that predicts a continuous numeric value, such as a price or a temperature, based on the input features of the data.

Classification

A supervised learning task that predicts which category or class an item belongs to, such as spam versus not spam in an email dataset.

Clustering

An unsupervised learning technique that groups similar data points together based on feature similarity, without using any predefined labels.

Overfitting

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

Automated machine learning

An Azure Machine Learning feature that automatically tries many algorithms and preprocessing options to find the best model for a dataset with minimal code.

Image classification

A computer vision task that assigns one or more class labels to an entire image, such as tagging a photograph as containing a dog.

Object detection

A computer vision task that identifies the location of objects in an image using bounding boxes and assigns a class label to each detected object.

Semantic segmentation

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.

Optical character recognition

A computer vision technique that reads printed or handwritten text in images and documents and converts it into machine-readable characters.

Azure AI Vision

An Azure cognitive service providing prebuilt computer vision capabilities such as image tagging, captioning, object detection, and OCR through simple API calls.

Custom Vision

An Azure service that lets you train custom image classification and object detection models using your own set of labeled images.

Sentiment analysis

A natural language processing task that evaluates text and returns a sentiment label such as positive, neutral, or negative, usually with confidence scores.

Key phrase extraction

A natural language processing task that identifies the main concepts or topics in a body of text, returning a list of significant terms and phrases.

Named entity recognition

A natural language processing task that finds and classifies entities in text, such as people, places, organizations, dates, and quantities.

Azure AI Language

An Azure service that bundles NLP capabilities including sentiment analysis, key phrase extraction, entity recognition, summarization, and question answering behind one resource.

Speech synthesis

The text-to-speech capability of Azure AI Speech that converts written text into spoken audio using configurable synthetic voices.

Question answering

An Azure AI Language capability that builds a knowledge base of question-and-answer pairs and returns the best answer to a user's natural language question.

Large language model

A generative AI model trained on vast amounts of text to predict the next token, enabling it to write, summarize, translate, and answer questions.

Prompt

The input text or instruction given to a generative AI model that describes the task and guides the response the model produces.

Retrieval augmented generation

A technique that grounds a generative model's response by retrieving relevant data from an external source and including that data in the prompt.

Hallucination

A generative AI failure where the model produces plausible-sounding but factually incorrect or fabricated information that is not supported by source data.

Azure OpenAI Service

An Azure service that provides access to OpenAI generative models such as GPT, with Azure security, compliance, networking, and content filtering built in.

Content filtering

A safety feature in Azure OpenAI that detects and blocks harmful content categories such as hate, violence, sexual content, and self-harm in prompts and responses.