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Monitoring and Observability premium Azure Monitor Azure Monitor is Microsoft’s unified observability service for collecting, analyzing, and acting on telemetry from Azure, hybrid, and application workloads. It brings together metrics, logs, traces, events, alerts, workbooks, and Application Insights capabilities so teams can understand health, performance, and reliability. Monitoring fundamentals 3 commands Aliases: Azure Monitor, Monitor, Azure observability, Log Analytics, Application Insights, Azure metrics and logs Quick peek Open full term page

AI and Machine Learning premium Azure OpenAI Azure OpenAI provides OpenAI model capabilities through Azure resources, deployments, identity, networking, quota, and monitoring controls. Azure OpenAI intermediate 8 commands Aliases: Azure OpenAI Service Quick peek Open full term page

Developer Tools premium Azure Developer CLI An open-source developer command-line tool that accelerates provisioning, deployment, pipeline setup, and monitoring for application resources on Azure. Developer workflow fundamentals 6 commands Aliases: azd Quick peek Open full term page

Analytics premium Azure Data Explorer A fully managed, high-performance analytics service for near-real-time analysis of large telemetry, log, event, and time-series datasets. Real-time analytics intermediate 5 commands Aliases: ADX, Kusto Quick peek Open full term page

Databases premium Azure Database for MySQL Flexible Server A fully managed Azure database service for MySQL workloads with configurable compute, storage, backups, networking, maintenance, and high availability options. Managed MySQL intermediate 5 commands Aliases: MySQL Flexible Server 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 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

Storage premium Azure Elastic SAN A cloud-native Azure storage area network service that provides scalable, cost-effective, high-performance block storage volumes for compute workloads. Block storage intermediate 5 commands Aliases: Elastic SAN Quick peek Open full term page

Management and Governance premium Azure Advisor Azure Advisor is the Azure recommendation service that reviews resource configuration and usage signals, then suggests best-practice actions for healthier cloud deployments. In Azure, teams encounter it when platform teams need a prioritized backlog for cost savings, reliability fixes, security hardening, performance improvements, and operational excellence. The useful question is what behavior it proves, who Optimization fundamentals 4 commands Aliases: Advisor, Azure Advisor recommendations, Advisor score 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 Language Azure AI Language is the Azure natural-language processing service for analyzing and understanding text with prebuilt and customizable language capabilities. AI services intermediate 4 commands Aliases: Azure Language, Azure Language in Foundry Tools, Azure Language service, Language in Foundry Tools, Language service 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 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

AI and Machine Learning premium Azure AI Search service Azure AI Search service is the Azure resource that hosts Azure AI Search indexes, indexers, skillsets, keys, capacity, networking, and query endpoints. AI platform and search intermediate 4 commands Aliases: AI Search resource, AI Search service, Azure Search service, Search service, 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

Monitoring and Observability premium Azure dashboard A customizable Azure portal view for organizing resource tiles, metrics, charts, links, and operational context into private or shared dashboards. Visualization fundamentals 4 commands Aliases: Azure portal dashboard, Portal dashboard Quick peek Open full term page

Analytics premium Data Factory monitoring The operational view of Data Factory pipeline runs, activity runs, trigger history, integration runtime health, metrics, logs, and alerts. Data integration and orchestration Intermediate 4 commands Aliases: Data Factory monitoring, ADF monitoring, data factory monitoring Quick peek Open full term page

AI and Machine Learning top-250-pre130-priority-upgraded Customer managed key for Azure OpenAI Customer managed key for Azure OpenAI is a production Azure concept tied to Azure OpenAI. Azure OpenAI advanced 4 commands Aliases: Customer-managed key for Azure OpenAI, Customer managed key for Azure OpenAI Quick peek Open full term page

Monitoring and Observability field-manual-ready Monitoring Reader Monitoring Reader is an Azure role that lets users view monitoring data and settings without broad resource management permissions. Azure Monitor RBAC fundamentals 4 commands Aliases: Azure Monitor Reader, Monitoring Reader, monitoring RBAC Quick peek Open full term page

Analytics learning-path-anchor Synapse Spark pool A Synapse Spark pool is the workspace compute definition Azure Synapse uses to start Apache Spark sessions. It records node size, node count, autoscale behavior, runtime version, packages, and idle timeout so notebooks, Spark jobs, and pipelines get repeatable distributed processing. Synapse Analytics fundamentals 8 commands Aliases: Apache Spark pool, Spark pool, Synapse Apache Spark pool, serverless Spark pool Quick peek Open full term page

Monitoring and Observability learning-path-anchor Telemetry events Telemetry events are breadcrumbs that say something meaningful happened in an application. They are different from raw logs because they usually have a clear event name and structured properties. A checkout completed, a document uploaded, a feature flag changed, or a device registered can all be telemetry events. In Azure Monitor Application Insights, these events help teams understand behavior, investigate incidents, and measure product outcomes. Good events are intentional, consistently named, and tied to the que Application data advanced 5 commands Aliases: event telemetry, custom events, Application Insights events, Azure Monitor events 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 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

Databases learning-path-anchor Temporary bytes Disk space used by temporary files created during queries, sorts, hashes, or joins. PostgreSQL advanced 4 commands Aliases: Temporary bytes, temporary bytes, Azure Temporary bytes, temp_bytes, Temporary Files Size, PostgreSQL temporary files Quick peek Open full term page

Monitoring and Observability learning-path-anchor Throughput Throughput is the amount of work a system completes over time, such as requests, messages, bytes, events, or transactions per second. In Azure, teams measure it with service metrics, logs, and capacity settings to judge whether an app, database, storage account, or messaging pipeline can sustain demand. Performance intermediate 4 commands Aliases: Throughput, throughput, Azure Throughput, Microsoft Learn Throughput, requests per second, events per second, transactions per second, bytes per second, work completed over time Quick peek Open full term page

Databases premium Cosmos DB trigger Cosmos DB trigger is a registered JavaScript pre-trigger or post-trigger for Cosmos DB for NoSQL operations inside a container. It runs server-side logic around an item operation when application code explicitly asks Cosmos DB to use it. You see it when teams maintain legacy NoSQL scripts, troubleshoot write behavior, or compare triggers with change feed and Azure Functions. The production check is whether the trigger is necessary, explicitly invoked, partition-scoped, tested, and safer than handling the logic in application code. Document the decision in code, templates, metrics, and runbooks. Azure Cosmos DB intermediate 8 commands Aliases: No aliases yet Quick peek Open full term page

Databases premium Cosmos DB physical partition Cosmos DB physical partition means a fully managed internal storage and compute partition that hosts ranges of logical partition keys in Azure Cosmos DB. In plain English, it is the thing developers and operators check when they need to understand how data access really works. It connects the application model to automatic scale-out, replica placement, RU distribution, and operational signals such as hot partition diagnostics. For a production team, it turns vague database talk into a specific thing to inspect in the portal, SDK code, templates, metrics, and incident notes. Azure Cosmos DB intermediate 7 commands Aliases: No aliases yet Quick peek Open full term page

Databases premium Cosmos DB provisioned throughput Cosmos DB provisioned throughput means the request-unit-per-second capacity reserved for a database or container, either manually or with autoscale behavior in Azure Cosmos DB. In plain English, it is the thing developers and operators check when they need to understand how data access really works. It connects the application model to capacity planning, throttling prevention, RU allocation, cost control, and predictable performance for operational workloads. For a production team, it turns vague database talk into a specific thing to inspect in the portal, SDK code, templates, metrics, and incident notes. Azure Cosmos DB intermediate 7 commands Aliases: No aliases yet Quick peek Open full term page

Databases premium Cosmos DB spatial index Cosmos DB spatial index is an indexing-policy entry that helps Cosmos DB for NoSQL query valid GeoJSON points, lines, polygons, and multipolygons efficiently. It makes location-aware queries practical without scanning every item in a container. You see it when applications store coordinates, service areas, delivery zones, device positions, or map features in JSON items. The production check is whether GeoJSON shape, indexed path, query function, and partition strategy all line up. Document the decision in code, templates, metrics, and runbooks. Azure Cosmos DB intermediate 7 commands Aliases: No aliases yet Quick peek Open full term page

Databases premium Cosmos DB vector embedding policy Cosmos DB vector embedding policy is the container policy that defines vector paths, dimensions, data type, and distance function for embeddings stored in Cosmos DB for NoSQL. It tells Cosmos DB which item properties contain embeddings and how those vectors should be interpreted. You see it when teams build semantic search, recommendation, image similarity, or retrieval-augmented generation features on Cosmos DB data. The production check is whether the vector path, dimensions, data type, and distance function are correct before production data lands. Document the decision in code, templates, metrics, and runbooks. Azure Cosmos DB intermediate 7 commands Aliases: No aliases yet Quick peek Open full term page

Databases premium Cosmos DB vector index Cosmos DB vector index is an indexing-policy configuration that improves vector-search latency and RU efficiency for embeddings in Cosmos DB for NoSQL. It helps Cosmos DB find similar vectors without comparing every embedding in a container one by one. You see it when queries use VectorDistance, hybrid search combines text and vectors, or teams tune flat, quantized flat, and DiskANN-style index choices. The production check is whether the index type, vector path, dimensions, filters, and recall target fit the workload. Document the decision in code, templates, metrics, and runbooks. Azure Cosmos DB intermediate 7 commands Aliases: No aliases yet Quick peek Open full term page

Analytics premium Databricks job A scheduled or triggered Databricks workflow that runs one or more tasks using configured compute, parameters, retries, notifications, and access controls. Azure Databricks fundamentals 7 commands Aliases: Databricks workflow job, Lakeflow Job, Databricks Jobs Quick peek Open full term page

Integration premium Capacity unit A capacity measure used by Event Hubs Dedicated clusters. Messaging and eventing intermediate 6 commands Aliases: No aliases yet Quick peek Open full term page

Databases premium Cosmos DB serverless Cosmos DB serverless is the Cosmos DB capacity mode that bills for request units consumed and storage used instead of pre-provisioned RU/s. It lets intermittent or unpredictable workloads use Cosmos DB without reserving throughput all day. You see it when a new account is created, a workload has bursty usage, or a team compares serverless with provisioned throughput. The production check is whether consumption billing, throughput limits, regional design, and workload shape fit the production objective. Document the decision in code, templates, metrics, and runbooks. Azure Cosmos DB intermediate 6 commands Aliases: No aliases yet Quick peek Open full term page

Databases premium Cosmos DB serverless account Cosmos DB serverless account is a Cosmos DB account created with the serverless capacity mode so databases and containers are billed by consumed request units and storage. It turns serverless billing into an account-level design choice rather than a per-container toggle. You see it when teams create accounts for prototypes, seasonal apps, low-duty-cycle workloads, or services with uncertain early demand. The production check is whether the account capacity mode supports the API, scale target, compliance need, and migration plan. Document the decision in code, templates, metrics, and runbooks. Azure Cosmos DB intermediate 6 commands Aliases: No aliases yet Quick peek Open full term page

Databases premium Cosmos DB TTL Cosmos DB TTL is the time-to-live setting that lets Cosmos DB automatically delete items after a configured number of seconds. It turns data expiration into a database policy instead of a manual cleanup job. You see it when containers store sessions, events, temporary search data, soft-delete markers, or records with explicit retention windows. The production check is whether container-level TTL, item overrides, change-feed behavior, and recovery expectations match the business retention rule. Document the decision in code, templates, metrics, and runbooks. Azure Cosmos DB intermediate 6 commands Aliases: No aliases yet Quick peek Open full term page

Analytics premium Databricks cluster A Databricks compute resource that runs notebooks, jobs, libraries, Spark workloads, and data processing tasks using configured runtime, workers, policies, and access controls. Azure Databricks fundamentals 6 commands Aliases: Databricks compute, Azure Databricks compute cluster, classic cluster Quick peek Open full term page

Analytics premium Databricks cluster policy A Databricks governance object that limits which cluster settings users can choose, helping control cost, security, runtime consistency, and workload standards. Azure Databricks intermediate 6 commands Aliases: Databricks compute policy, cluster policy, compute policy Quick peek Open full term page

Networking premium DDoS Protection Azure DDoS Protection is a network-layer protection service that helps defend Azure public IP resources against distributed denial-of-service attacks using always-on traffic monitoring, adaptive mitigation, telemetry, and response capabilities. Network security intermediate 6 commands Aliases: Azure DDoS Protection, DDoS Network Protection, DDoS IP Protection, distributed denial of service protection Quick peek Open full term page

Databases premium DTU model The DTU model is an Azure SQL Database purchasing model that bundles compute, memory, and I/O into service tiers and service objectives such as Basic, Standard, and Premium. Azure SQL fundamentals 6 commands Aliases: DTU purchasing model, Azure SQL DTU model Quick peek Open full term page

Integration premium Event subscription An Event subscription is an Event Grid configuration that selects events from a source and delivers matching events to a destination endpoint. Teams use it to route storage, resource, custom, or partner events to the correct webhook, function, queue, topic, or event handler. It is not the event source itself, an Azure subscription, an Event Hubs consumer group, or proof that the destination successfully processed every event. In production, confirm source scope, event types, filters, endpoint URL or resource ID, authentication method, dead-letter destination, retry settings, diagnostic logs, and receiving application owner before treating the design as healthy or ready. Event Grid intermediate 6 commands Aliases: Event Grid subscription, event routing subscription, event delivery subscription Quick peek Open full term page

Analytics premium Event trigger An Event trigger starts an Azure Data Factory or Synapse pipeline in response to supported storage events such as blob creation or deletion. Teams use it to start pipelines automatically when files land, folders change, or storage events indicate that a data integration workflow should run. It is not a scheduled trigger, a tumbling-window trigger, an Event Hubs consumer, or a guarantee that the file is complete and ready for every downstream transformation. In production, confirm factory name, trigger state, storage scope, blob path filters, event type, target pipeline, parameter mapping, managed identity access, publish branch state, and run history. Data Factory intermediate 6 commands Aliases: storage event trigger, Data Factory event trigger, event-based trigger Quick peek Open full term page

Containers premium Event-driven job An Event-driven job in Azure Container Apps starts job executions when a configured scale rule detects work from an event source. Teams use it to run finite container work such as queue processing, batch enrichment, report generation, or cleanup only when events or messages are waiting. It is not a continuously running container app, an AKS deployment, a scheduled job, or a guarantee that every event is processed exactly once. In production, confirm job trigger type, scale rule, minimum and maximum executions, polling interval, image version, secret references, managed identity, execution status, logs, retries, and queue depth before treating the. Azure Container Apps intermediate 6 commands Aliases: Container Apps event-driven job, KEDA job, event-triggered container job Quick peek Open full term page

Containers premium Event-driven scale rule An Event-driven scale rule is a Container Apps scaling configuration that uses event-source metrics to decide how many replicas or job executions to run. Teams use it to connect queue length, event backlog, HTTP load, Kafka lag, or other scaler metadata to automatic scaling decisions. It is not the event source itself, a Kubernetes HPA object, a fixed replica count, or a complete guarantee that the application processes events correctly. In production, confirm scaler type, metadata names, authentication method, min and max replicas, polling interval, cooldown behavior, event source metrics, container revision, and downstream capacity before treating the design as. Azure Container Apps intermediate 6 commands Aliases: KEDA scale rule, Container Apps event scale rule, custom scale rule Quick peek Open full term page

Databases premium Eventual consistency Eventual consistency is the weakest Azure Cosmos DB consistency level, where reads can return a subset of writes and all writes become available eventually. Teams use it to favor low-latency, high-availability reads for workloads that can tolerate temporarily stale or out-of-order data. It is not strong consistency, session consistency, a conflict-resolution strategy, a cache setting, or permission to ignore user-facing correctness requirements. In production, confirm account consistency setting, client overrides, read region, write region, session tokens, replication latency, stale-read tolerance, conflict policy, and application workflows that read after writes before treating the design as healthy or ready for release. Azure Cosmos DB intermediate 6 commands Aliases: eventual consistency level, Cosmos DB eventual consistency, eventually consistent reads Quick peek Open full term page

Analytics premium Execute Pipeline activity The Execute Pipeline activity lets an Azure Data Factory or Synapse pipeline invoke another pipeline as part of a control-flow workflow. Teams use it to compose reusable parent and child pipelines, pass parameters, and control whether the parent waits for the child pipeline to finish. It is not a copy activity, a trigger, a stored procedure call, or a guarantee that child pipelines share variables or error handling automatically. In production, confirm parent pipeline, child pipeline, parameter map, wait-on-completion setting, activity output, run IDs, failure policy, publish branch, and monitoring evidence for both runs before treating the design as. Data integration intermediate 6 commands Aliases: Execute Pipeline, child pipeline activity, pipeline invocation activity Quick peek Open full term page

AI and Machine Learning premium Experiment An Experiment in Azure Machine Learning groups related runs so parameters, metrics, artifacts, models, and lineage can be tracked and compared. Teams use it to organize model training, evaluation, and tuning work so teams can compare runs and reproduce how a model version was produced. It is not a deployed endpoint, a registered model, a notebook file, a compute cluster, or proof that a model is fair, secure, or production-ready. In production, confirm workspace, experiment name, run IDs, parameters, metrics, artifacts, data version, environment, compute target, model registration, owner, and promotion criteria before treating the design as healthy or ready. Machine learning intermediate 6 commands Aliases: Azure ML experiment, MLflow experiment, machine learning experiment Quick peek Open full term page

Analytics premium Expression language Expression language is the Azure Data Factory and Azure Synapse syntax used to build dynamic values, parameters, conditions, and function calls inside pipeline definitions. Teams use it to make pipeline paths, dates, conditions, parameters, linked-service values, and activity settings change at runtime instead of being hard-coded. It is not general programming code, a SQL dialect, a security boundary, or a guarantee that dynamic values are valid for every dataset and trigger context. In production, confirm expression syntax, parameter names, variable scope, activity output references, trigger metadata, evaluated values, published JSON, debug runs, run IDs, and downstream paths or queries before. Data Factory intermediate 6 commands Aliases: ADF expression language, Data Factory expression language, pipeline expression language, dynamic content Quick peek Open full term page