Azure AI-102 key terms

A single Azure resource that exposes many prebuilt AI capabilities, such as vision, language, speech, and translation, through one endpoint and one set of credentials, which simplifies billing and access management.

The HTTPS URL a client calls plus a secret key sent with each request to prove identity. Keys should be kept in a secret store rather than hard-coded in application source.

An automatically managed identity in Microsoft Entra ID that an Azure resource uses to call other services without storing secrets, usually paired with a role assignment granting access to an AI resource.

A network interface placed inside a virtual network that gives an Azure AI resource a private IP address, so client applications reach it without traffic crossing the public internet.

A workspace inside Azure AI Foundry that groups model deployments, data connections, agents, and evaluations for a team, and provides playgrounds for testing prompts and models.

A named, hosted instance of a specific model with a chosen SKU, region, and capacity. Applications call the deployment name rather than the underlying model name, so models can be swapped.

The unit of text a language model reads and generates; common words may be one token while rarer words split into several. Context limits and usage billing are measured in tokens.

A sampling parameter that controls randomness in generated text. Lower values produce more deterministic, focused output, while higher values produce more varied and creative responses.

An instruction block sent before the user conversation that sets the model's role, tone, rules, and constraints, and typically carries more weight than later turns in a chat.

The practice of designing instructions, context, and examples so a generative model returns accurate, well-formatted answers, including few-shot examples and explicit output format guidance.

Supplying relevant, verifiable content such as retrieved documents or search results inside the prompt, so the model's answer is based on that data rather than only its training knowledge.

A pattern that searches an index for relevant passages, inserts them into the prompt, and asks the model to answer using them, improving factual accuracy without retraining the model.

A numeric vector that represents the meaning of text, produced by an embedding model, so that passages with similar meaning have nearby vectors and can be compared by similarity search.

A data structure that stores embeddings and answers similarity queries, letting an application find passages semantically related to a user question before sending them to a language model.

Additional training of a base model on your own labeled examples so it better follows a required style, format, or task. It changes model weights and costs more than prompt engineering.

A unit of reserved model capacity purchased in advance. Provisioned deployments give predictable latency and throughput for steady high-volume workloads, unlike pay-as-you-go standard deployments.

A safety capability that detects attempts to manipulate a model, such as jailbreak instructions or hidden injected text inside supplied documents, and flags the request so it can be blocked.

Built-in classification of prompts and completions into categories such as hate, violence, sexual, and self-harm, with severity thresholds that can block or annotate the content.

A software component that combines a language model with instructions, tools, and knowledge so it can plan steps, take actions, and work toward a user's goal across multiple turns.

A thread stores the messages of one conversation with an agent; a run is a single execution that processes that thread, calls tools as needed, and appends results back to it.

A capability the model can request by returning a structured call, such as a custom function, web search, or code execution. The host application runs it and returns output to the model.

A managed Azure service that hosts agents, persists conversation threads, runs tools such as file search and Bing grounding, and connects to models deployed in Azure AI Foundry.

An agent exposed to another agent as a callable tool, so an orchestrator agent can delegate a subtask and merge the returned answer into its own final response.

A prebuilt Azure AI Vision capability that returns captions, dense captions, tags, detected objects, people, and readable text for an image supplied by the application.

An optical character recognition capability in Azure AI Vision that extracts printed and handwritten text from images and documents, returning lines and words with bounding boxes and confidence.

Training a model on your own labeled images so it predicts categories for a whole image, or locates objects with bounding boxes in the object detection variant, for a specific domain.

A vision capability that analyzes streams from fixed cameras to count people, measure zone occupancy, and track movement across lines, supporting retail, safety, and facility scenarios.

A resource offering prebuilt text analytics such as entity recognition, PII detection, summarization, and language detection, plus customizable conversational understanding and question answering features.

A prebuilt text analysis feature that finds and classifies entities within text, such as people, organizations, locations, dates, quantities, and web addresses.

Identifies personal data such as names, phone numbers, email addresses, and locations in text, and can mask or redact it to support privacy and compliance requirements.

A customizable language feature where you label utterances with intents and entities, train and publish a model, then have client apps extract user goals from conversational input.

A feature that builds a knowledge base from FAQ pages, documents, or manual pairs and returns the best matching answer with a confidence score and optional follow-up prompts.

A service that translates text between languages, detects the source language, and supports custom glossaries plus asynchronous document translation for batch workloads.

A service for speech-to-text transcription, text-to-speech synthesis with neural voices, speech translation, and pronunciation assessment, consumed through SDKs or REST endpoints.

A cloud search service that stores content in indexes, uses indexers and skillsets to enrich data, and supports keyword, vector, and hybrid queries for applications and retrieval-augmented generation.

A crawler-like component in Azure AI Search that connects to a data source, reads documents, optionally runs a skillset over each one, and writes the enriched output into a search index.

A pipeline of enrichment steps, such as OCR, entity recognition, key phrase extraction, or embedding generation, applied by an indexer to each document before indexing.

A query feature in Azure AI Search that reorders results using language understanding, promoting passages that best answer the question and enabling captions and extractive answers.

A service that extracts text, tables, and key-value fields from forms, invoices, receipts, and identity documents using prebuilt models, or from your own layouts using custom models.