Azure AI-102 flashcards

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

Multi-service AI resource

A single Azure resource of kind AIServices that exposes one endpoint and one key shared across many AI services, simplifying billing and role assignment.

Cognitive Services User role

A Microsoft Entra ID role that authorizes an identity to call Azure AI service endpoints without account keys; assign it at resource or resource-group scope.

HTTP 429 throttling response

Indicates the request rate or quota limit was exceeded; retry after the Retry-After interval with exponential backoff, or raise the pricing tier capacity.

Private endpoint for AI services

Assigns a private IP address from your virtual network to the AI resource; combined with public network access disabled, all traffic stays inside the network.

Free F0 pricing tier

A capped free tier allowing a limited number of monthly transactions; only one F0 resource per service type is permitted per subscription.

Temperature parameter

A 0 to 2 sampling control; lower values make completions more deterministic and focused, while higher values increase diversity and creativity in generated text.

max_tokens completion limit

Caps the number of tokens generated in the response only; prompt tokens are not counted here but still consume the model context window.

System message role

The first message in a chat conversation, used to set persona, tone, and constraints; it takes precedence over later user instructions.

Content filter severity levels

Filters score hate, sexual, violence, and self-harm categories as safe, low, medium, or high, blocking medium and above by default for both prompts and completions.

Retrieval-augmented generation pattern

Grounds model answers by retrieving relevant documents from an index, injecting them into the prompt, and returning citations that reduce unsupported claims.

Text embedding vectors

Numeric representations of text where semantically similar content has high cosine similarity; used for vector search, clustering, and recommendation scenarios.

Agent thread

A conversation session in Azure AI Agent Service that stores messages in order; runs execute against it and append the model's responses.

Submit tool outputs

When a run reports requires_action, the client executes the requested function and returns the result so the same run can continue.

File search tool

Grounds an agent on uploaded documents by chunking and embedding them into a vector store, then returning retrieved content with citations.

Code interpreter tool

A sandboxed Python environment that lets an agent perform calculations, data analysis, and chart generation, with generated files available for download.

Function calling schema

A JSON description of a function's name, purpose, and typed parameters that the model uses to decide when to call it and which arguments to supply.

Bing grounding tool

Lets an agent answer with real-time web results; responses include citations with URLs and the project needs a Bing Search connection.

Image Analysis visual features

Options such as caption, dense captions, tags, objects, read, and people that request specific insights from an image in one call.

Custom Vision classification project

Trains a model to assign labels to an entire image; choose multilabel when one image can carry more than one tag.

Object detection versus classification

Detection returns bounding boxes plus a label for each object found; classification returns only a label describing the whole image.

Face identification versus verification

Verification is a 1:1 check that two faces belong to the same person; identification is a 1:N search against a person group.

Compact domain models

Custom Vision models exported as ONNX, TensorFlow, CoreML, or Docker packages for offline edge inference, with slightly lower accuracy than cloud models.

Opinion mining in sentiment

Aspect-based sentiment analysis that attaches positive or negative assessments to specific targets mentioned in text, such as food rated great.

PII redaction policy

Controls how detected personal data is masked in responses, either by replacing characters with asterisks or by substituting the entity type name.

CLU intents and entities

Conversational language understanding classifies an utterance into a named intent and extracts entity values; project quality depends on varied labeled utterances.

Custom question answering knowledge base

A store of question and answer pairs with alternate phrasings that returns the best matching answer and supports follow-up prompts and chit-chat.

Extractive versus abstractive summary

Extractive summarization selects existing sentences from the source document; abstractive summarization generates new sentences that may reword the content.

Search index field attributes

Searchable enables full-text matching, filterable enables OData filters, sortable enables ordering, facetable enables counts, and retrievable returns values in results.

Indexer and skillset pipeline

An indexer pulls data from a data source, runs an optional skillset that enriches each document, and writes mapped output into the search index.

Semantic ranker

An optional Azure AI Search feature that re-ranks the top results of a query using a language model for better relevance, handling up to 50 results.

Hybrid vector search

Runs keyword and vector queries together and fuses the ranked lists with reciprocal rank fusion, improving recall compared with either method alone.

Prebuilt invoice model

A Document Intelligence model that extracts vendor, invoice date, totals, taxes, and line items from invoices without any custom training.