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Analytics premium Azure Databricks A unified, open analytics platform on Azure for building, deploying, sharing, and maintaining enterprise data, analytics, and AI solutions at scale. Data engineering and AI intermediate 5 commands Aliases: Databricks on Azure Quick peek Open full term page

AI and Machine Learning command-rich Data source in AI Search Data source in AI Search is documented by Microsoft as part of the Azure AI Search area in Azure. Azure AI Search intermediate 6 commands Aliases: No aliases yet Quick peek Open full term page

AI and Machine Learning premium Azure AI Search data source Azure AI Search data source is the connection definition an Azure AI Search indexer uses to read content from a supported external data store. AI platform and search intermediate 4 commands Aliases: Search data source, SearchIndexerDataSourceConnection, data source connection, indexer data source, search data source connection Quick peek Open full term page

Analytics learning-path-anchor Databricks metastore The top-level Unity Catalog container for catalogs, schemas, tables, volumes, models, functions, and governance permissions. Databricks intermediate 4 commands Aliases: Unity Catalog metastore, Databricks Unity Catalog metastore, metastore Quick peek Open full term page

Analytics learning-path-anchor Databricks model serving A managed serving endpoint pattern for exposing Databricks models or AI workloads for online inference with operational controls. Databricks intermediate 4 commands Aliases: Databricks Model Serving, serving endpoint, model serving endpoint Quick peek Open full term page

Analytics learning-path-anchor Databricks schema A Unity Catalog namespace under a catalog that groups tables, views, volumes, models, functions, and permissions. Databricks fundamentals 4 commands Aliases: Unity Catalog schema, Databricks database schema, schema in Unity Catalog Quick peek Open full term page

Storage learning-path-anchor Data Lake storage account A storage feature or access model in Data Lake Storage Gen2 that helps teams store, protect, move, and govern application or analytics data with clearer ownership, safety, and operational context. Data Lake Storage Gen2 advanced 19 commands Aliases: No aliases yet Quick peek Open full term page

Storage premium Data Lake analytics workload A production analytics workload that stores, transforms, governs, and serves large data sets from a data lake using Azure services such as ADLS Gen2, Data Factory, Databricks, Synapse, or Fabric. Data Lake Storage Intermediate 6 commands Aliases: No aliases yet Quick peek Open full term page

Storage premium Data Lake curated zone The governed, consumer-ready area of a data lake where cleaned, conformed, and documented datasets are served for reporting, analytics, AI, or downstream applications. Data Lake Storage Gen2 Intermediate 6 commands Aliases: No aliases yet Quick peek Open full term page

Analytics premium Databricks access connector A first-party Azure resource that gives Azure Databricks a managed identity for Unity Catalog storage credentials and other governed service access. Azure Databricks intermediate 6 commands Aliases: Access Connector for Azure Databricks, Azure Databricks access connector, Databricks managed identity connector Quick peek Open full term page

Analytics premium External data source An External data source is a database object that defines the location and connection information used by SQL engines to access external data. Teams use it to point serverless SQL, dedicated SQL pools, or PolyBase-style queries to data stored outside the database, such as Azure Storage or Data Lake paths. It is not the external table schema, the file format definition, the storage account itself, or proof that the credential can read every file under the path. Synapse SQL and data virtualization intermediate 6 commands Aliases: CREATE EXTERNAL DATA SOURCE, PolyBase external data source, Synapse external data source Quick peek Open full term page

AI and Machine Learning premium Azure AI Search index An Azure AI Search index is the place where your application searches. It is not just a pointer to a database or blob container. It is a separate searchable structure with documents, fields, and rules for how text, filters, facets, semantic ranking, and vectors behave. If the index schema is wrong, the search experience feels wrong even when the source data is good. Developers and operators use indexes to make. AI platform and search intermediate 5 commands Aliases: Azure AI Search index, search index, AI Search index, azure-ai-search-index Quick peek Open full term page

AI and Machine Learning premium Azure AI Search indexer An Azure AI Search indexer is the automated worker that fills a search index from another data source. Instead of your application pushing every document into Azure AI Search, the indexer pulls from places such as Blob Storage, Azure SQL, Cosmos DB, or Data Lake Storage. It can map fields, detect changes, crack documents, and run enrichment skills before writing searchable documents. It is useful when source data changes regularly. AI platform and search intermediate 5 commands Aliases: Azure AI Search indexer, search indexer, indexer, azure-ai-search-indexer Quick peek Open full term page

Databases premium Azure Database for PostgreSQL Flexible Server A fully managed Azure PostgreSQL service with configurable compute, storage, backups, networking, maintenance, extensions, and high availability options. Managed PostgreSQL intermediate 5 commands Aliases: PostgreSQL Flexible Server Quick peek Open full term page

Analytics premium Azure Databricks Unity Catalog A unified governance layer in Azure Databricks for data and AI assets, including catalogs, schemas, tables, volumes, models, privileges, and lineage. Data governance intermediate 5 commands Aliases: Unity Catalog Quick peek Open full term page

Analytics premium Data flow cluster The managed Spark compute that Azure Data Factory or Synapse uses to execute a mapping data flow during a debug session or scheduled pipeline run. Data Factory Intermediate 5 commands Aliases: No aliases yet Quick peek Open full term page

Analytics premium Databricks workflow A Databricks workflow is a scheduled or triggered orchestration of tasks in Azure Databricks, commonly managed through Lakeflow Jobs, for running notebooks, SQL, pipelines, scripts, and production data workloads. Data engineering and analytics intermediate 5 commands Aliases: Databricks Jobs workflow, Lakeflow Jobs workflow, Databricks scheduled workflow, Databricks orchestration Quick peek Open full term page

Analytics premium Databricks workspace A Databricks workspace is the Azure resource and collaborative environment where teams create and operate notebooks, jobs, clusters, SQL warehouses, repositories, experiments, and governed data access. Analytics platform beginner 5 commands Aliases: Azure Databricks workspace, Databricks workspace resource, workspace resource, Databricks environment Quick peek Open full term page

Analytics premium Dataset parameter A dataset parameter is a named value on a Data Factory or Synapse dataset that lets pipelines pass runtime information such as folder, file, schema, table, or partition values into a reusable dataset definition. Data engineering and analytics intermediate 5 commands Aliases: ADF dataset parameter, Data Factory dataset parameter, parameterized dataset, dataset runtime value Quick peek Open full term page

AI and Machine Learning premium Search data source A search data source is an Azure AI Search indexer object that describes where external content comes from. It specifies the supported source type, connection information, credentials or identity, container or query, and sometimes change or deletion detection used by indexers. Azure AI Search fundamentals 5 commands Aliases: Azure AI Search data source, indexer data source, SearchIndexerDataSourceConnection, data source connection, Azure Search data source object Quick peek Open full term page

AI and Machine Learning premium Azure AI Foundry hub Azure AI Foundry hub is the classic Foundry hub resource used to group AI projects that share common security, data access, connections, and platform settings. In Azure, teams encounter it when teams need hub-based projects for selected Foundry and Azure Machine Learning scenarios such as fine-tuning, shared connections, and custom ML work. The useful question AI platform advanced 4 commands Aliases: AI Hub, Foundry AI Hub, hub-based project, Azure AI hub Quick peek Open full term page

AI and Machine Learning premium Azure AI Foundry project An Azure AI Foundry project is the working area where a team builds and organizes an AI application inside Microsoft Foundry. It keeps related agents, evaluations, files, indexes, tools, connections, and model usage together instead of scattering them across a shared portal. Think of it as the project boundary for one AI product, prototype, or team. It is useful because AI work quickly becomes messy: prompts, test data, model deployments, safety checks, and access decisions all need a home with ownership and repeatable operations. AI platform advanced 4 commands Aliases: AI Foundry project, AI project, Foundry project, Microsoft Foundry project Quick peek Open full term page

AI and Machine Learning premium Azure AI metrics Azure AI metrics is the measurable signals used to observe Azure AI applications, model endpoints, agents, evaluations, safety checks, and business outcomes. Azure AI services intermediate 4 commands Aliases: AI metrics, AI service metrics, Azure AI Metrics Advisor, Azure Monitor metrics for AI, Metrics Advisor, time series anomaly detection Quick peek Open full term page

AI and Machine Learning premium Azure AI Search Azure AI Search is a managed retrieval service for full-text, vector, hybrid, and semantic search across application and enterprise content. It provides search services, indexes, indexers, skillsets, ranking features, security controls, and APIs used by apps, copilots, and knowledge portals. Search fundamentals 4 commands Aliases: Azure AI Search, AI Search, Azure Cognitive Search, enterprise search, vector search, hybrid search, semantic search, Search service Quick peek Open full term page

AI and Machine Learning premium Azure AI Search skillset Azure AI Search skillset is a reusable Azure AI Search enrichment object that applies built-in or custom processing during indexer execution. AI platform and search intermediate 4 commands Aliases: AI enrichment skillset, Search skillset, cognitive skillset, skillset Quick peek Open full term page

AI and Machine Learning premium Azure AI services Azure AI services is the family of Azure cloud AI APIs and resources, surfaced through Foundry Tools, for vision, speech, language, translation, content, and generative scenarios. AI services fundamentals 4 commands Aliases: AI services, Azure AI services account, Cognitive Services, Foundry Tools Quick peek Open full term page

AI and Machine Learning premium Azure AI Speech The Azure speech service for converting speech to text, text to speech, speech translation, pronunciation assessment, and related speech-enabled capabilities. AI services intermediate 4 commands Aliases: Azure Speech, Speech in Foundry Tools, Speech service, Speech-to-text Quick peek Open full term page

AI and Machine Learning premium Azure AI Vision The Azure vision service that analyzes images and visual content for tasks such as OCR, object information, captions, tags, and image understanding. AI services intermediate 4 commands Aliases: Azure Vision, Computer Vision, Image Analysis, Vision in Foundry Tools Quick peek Open full term page

Management and Governance premium Azure data plane The Azure data plane is the part of Azure you use after a resource exists, when you interact with the capability that resource provides. Microsoft Learn anchors this term in Azure control plane and data plane, but this field-manual definition is intentionally wider than an older short glossary entry because the page must teach what to inspect, what can break, who owns the decision, and which evidence proves the Azure environment is behaving as intended. In field use, start with the technical boundary: Technically, data-plane operations are service-specific operations against. Azure Resource Manager fundamentals 4 commands Aliases: No aliases yet Quick peek Open full term page

Management and Governance premium Data classification Data classification is the practice of assigning logical labels or classes to data assets so teams understand sensitivity, business meaning, and handling requirements. Data governance intermediate 4 commands Aliases: Data classification Quick peek Open full term page

AI and Machine Learning premium Data drift A measurable change in production input data or feature distributions compared with the baseline data used to train or validate a model. Model monitoring and MLOps Intermediate 4 commands Aliases: feature drift, input data drift, model data drift Quick peek Open full term page

AI and Machine Learning premium Grounding data Grounding data is the source content, retrieved documents, indexed chunks, tool results, or external information supplied to a model so generated output can be based on approved facts. Azure OpenAI intermediate 4 commands Aliases: RAG grounding data, source grounding data, AI source material Quick peek Open full term page

AI and Machine Learning premium Machine Learning data asset A Machine Learning data asset is a named, versioned reference to data used by Azure Machine Learning jobs. It can point to files, folders, or MLTable definitions so teams can reuse training data without remembering raw storage paths. That context supports safer operational decisions. Machine learning intermediate 4 commands Aliases: Azure ML data asset, ML data asset Quick peek Open full term page

AI and Machine Learning premium ML data asset ML data asset is a named and versioned Azure Machine Learning reference to data used by jobs, pipelines, training, evaluation, or batch scoring. Teams should manage it with clear ownership, monitoring, rollback evidence, and production change discipline. Azure Machine Learning intermediate 4 commands Aliases: AML data asset, Azure ML data asset, MLTable asset, data asset, registered data asset Quick peek Open full term page

AI and Machine Learning premium ML datastore ML datastore is a workspace-level Azure Machine Learning connection to underlying storage used by jobs, data assets, pipelines, and experiments. Teams should manage it with clear ownership, monitoring, rollback evidence, and production change discipline. Azure Machine Learning intermediate 4 commands Aliases: AML datastore, Azure ML datastore, Machine Learning datastore, workspace datastore Quick peek Open full term page

AI and Machine Learning premium AI evaluation AI evaluation is the Microsoft Foundry process of testing a generative AI model, agent, or application against a dataset and measuring its quality, safety, and task performance with built-in or custom evaluators. Microsoft Foundry intermediate 3 commands Aliases: Azure AI evaluation, Foundry evaluation, generative AI evaluation, model evaluation Quick peek Open full term page

Monitoring and Observability premium AI service diagnostic settings AI service diagnostic settings are Azure Monitor settings on an Azure AI or Foundry Tools resource that route resource logs and platform metrics to destinations such as Log Analytics, Storage, or Event Hubs for investigation and retention. Azure Monitor intermediate 3 commands Aliases: Azure AI diagnostic settings, Foundry Tools diagnostic logging, Cognitive Services diagnostic settings, AI resource logs Quick peek Open full term page

AI and Machine Learning premium AI token An AI token is a unit of text processed by a language model. Azure AI and Foundry model quotas, cost, and rate limits commonly use tokens to measure prompt input, generated output, and throughput such as tokens per minute. Azure OpenAI and Foundry Models fundamentals 3 commands Aliases: model token, LLM token, input token, output token, TPM Quick peek Open full term page

Analytics learning-path-anchor Databricks MLflow The managed MLflow experience in Azure Databricks for experiments, runs, metrics, artifacts, model lineage, and MLOps evidence. Databricks fundamentals 4 commands Aliases: MLflow on Databricks, Databricks experiment tracking, MLflow tracking Quick peek Open full term page

Analytics learning-path-anchor Databricks notebook A collaborative Azure Databricks document for code, SQL, visualizations, exploration, jobs, and ML workflows attached to compute. Databricks fundamentals 4 commands Aliases: Azure Databricks notebook, Databricks workspace notebook, interactive notebook Quick peek Open full term page

Analytics learning-path-anchor Databricks Photon The native vectorized Azure Databricks engine for accelerating eligible SQL, DataFrame, ETL, and streaming workloads. Databricks intermediate 4 commands Aliases: Photon engine, Photon acceleration, Databricks vectorized engine Quick peek Open full term page

Analytics learning-path-anchor Databricks repo A workspace Git integration, now called Git folders, for versioning notebooks and files used in Databricks development and CI/CD. Databricks fundamentals 4 commands Aliases: Databricks Repos, Databricks Git folder, Azure Databricks Git folders, workspace Git folder Quick peek Open full term page

Analytics learning-path-anchor Databricks secret scope A named Databricks collection of secrets used to store credentials and grant controlled read or manage access to workloads. Databricks fundamentals 4 commands Aliases: Databricks secrets scope, secret scope, Azure Databricks secrets Quick peek Open full term page

Analytics learning-path-anchor Databricks SQL warehouse A Databricks SQL compute resource for interactive queries, dashboards, BI tools, and governed analytics workloads. Databricks intermediate 4 commands Aliases: SQL warehouse, Databricks SQL compute, serverless SQL warehouse Quick peek Open full term page

Analytics learning-path-anchor Databricks table A governed Databricks data object, commonly managed, external, or foreign, queried through Unity Catalog and optimized for analytics. Databricks fundamentals 4 commands Aliases: Unity Catalog table, Delta table in Databricks, Databricks managed table Quick peek Open full term page

AI and Machine Learning verified Responsible AI Microsoft Learn describes Responsible AI as an approach to developing, assessing, and deploying AI systems safely, ethically, and with trust. In Azure Machine Learning, it connects fairness, reliability, privacy, security, transparency, accountability, and operational tooling so teams can evaluate models before production use. Responsible AI fundamentals 8 commands Aliases: Responsible Artificial Intelligence, RAI, trustworthy AI, AI governance, responsible machine learning Quick peek Open full term page

AI and Machine Learning verified Responsible AI dashboard Microsoft Learn describes the Responsible AI dashboard as a single interface for putting Responsible AI into practice in Azure Machine Learning. It brings together tools for model debugging, cohort analysis, interpretability, counterfactuals, causal insights, error analysis, and shareable scorecards for registered models. Azure Machine Learning intermediate 7 commands Aliases: RAI dashboard, Responsible AI insights, model debugging dashboard, Responsible AI scorecard, fairness dashboard Quick peek Open full term page

AI and Machine Learning verified Provisioned throughput for AI Provisioned throughput for AI in Microsoft Foundry allocates provisioned throughput units to supported model deployments. PTUs give reserved processing capacity for predictable latency and throughput, with deployment choices such as regional, data-zone, or global provisioned capacity and separate reservation-based billing options. Microsoft Foundry advanced 6 commands Aliases: No aliases yet Quick peek Open full term page

AI and Machine Learning field-manual-complete Private endpoint for AI service A private endpoint for an AI service is the private network doorway to an Azure AI services account. Applications still call the service endpoint they are configured to use, but clients inside the linked network can resolve that name to a private IP instead of reaching across a public path. It is useful when document processing, language analysis, speech, content safety, or vision workloads handle sensitive data. It does not replace authentication, keys, managed identity, or responsible AI controls. AI platform and search intermediate 5 commands Aliases: No aliases yet Quick peek Open full term page

AI and Machine Learning verified Prompt engineering Prompt engineering is the craft of asking an AI model for work in a way it can follow reliably. It includes clear instructions, examples, context, boundaries, and required output formats. In Azure OpenAI and Foundry applications, prompt engineering is not magic phrasing; it is application design. A strong prompt explains the task, what evidence to use, what to avoid, and how success will be measured. It still needs testing because one good example does not prove the prompt works everywhere. AI platform and search intermediate 5 commands Aliases: No aliases yet Quick peek Open full term page