Course catalog

Cloud platform foundations

1. 01

   From virtual machines to serverless

   Compute

   Compare the responsibility boundaries, scaling models, and operations cost of VMs, containers, App Service, and Functions.

   AZ-104 / AZ-305 · 3 lessons · 0/3

   Lessons and context

   1. VM basics: responsibility boundaries, identity, and observabilityUse virtual machines (VMs), managed disks, availability, RBAC, and Azure Monitor to understand IaaS operating responsibilities.
   2. Container Apps: revisions, scaling, and a smaller platform surfaceUnderstand image delivery, revisions, ingress, KEDA scaling, and logging boundaries for managed container apps.
   3. Serverless operations: triggers, cold starts, and cost guardrailsUse Azure Functions to think about triggers, bindings, retries, observability, and budgets in event-driven compute.
   4. Related lab
   Open track
2. 02

   Objects, files, and recovery

   Storage

   Understand Blob, Files, lifecycle rules, encryption, backup, and cost tiers for application state and data governance.

   AZ-104 / AZ-305 · 3 lessons · 0/3

   Lessons and context

   1. Blob lifecycle: tiers, access, and retentionUse hot/cool tiers, lifecycle rules, Shared Access Signatures (SAS), RBAC, and immutable policies to govern object data.
   2. Azure Files: shared files and identity boundariesUnderstand SMB/NFS shares, hybrid access, permissions models, and migration scenarios.
   3. Backup and continuity: work backward from recovery targetsDesign for recoverability with recovery point objective (RPO), recovery time objective (RTO), backup vaults, soft delete, and drills.
   4. Related lab
   Open track
3. 03

   Segmentation, private access, and edge

   Network

   Build observable secure paths across VNet, DNS, Private Link, ExpressRoute, and Front Door.

   AZ-700 / AZ-305 · 3 lessons · 0/3

   Lessons and context

   1. VNet segmentation: subnets, routes, and minimal exposureUse address space, subnets, NSGs, route tables, and monitoring to understand Azure networking fundamentals.
   2. Private Link and DNS: private access is more than one endpointUnderstand private endpoints, private DNS zones, resolution paths, and PaaS data-plane protection.
   3. Edge and hybrid networking: Front Door, ExpressRoute, and failure domainsUse global ingress, private circuits, and multi-region design to understand performance, reliability, and cost tradeoffs.
   4. Related lab
   Open track
4. 04

   Relational, document, and search

   Database

   Choose Azure SQL, Cosmos DB, and Azure AI Search while designing backup, indexing, permissions, and availability.

   AZ-305 / Extended learning · 3 lessons · 0/3

   Lessons and context

   1. Azure SQL: day-to-day design for a managed relational databaseLearn Azure SQL through deployment models, identity, backups, service tiers, and auditing.
   2. Cosmos DB: partitioning, consistency, and throughputUnderstand partition keys, request units, replication, and consistency choices for a globally distributed NoSQL database.
   3. Azure AI Search: indexes, vectors, and citationsDesign full-text search, vector search, and RAG citations as a sidecar data plane to your database.
   4. Related lab
   Open track

Intelligence applications and models

1. 05

   RAG, agents, and multimodal input

   AI

   Place retrieval, citations, function tools, human handoff, and multimodal input inside responsible application architecture.

   Extended learning · 3 lessons · 0/3

   Lessons and context

   1. RAG: retrieval-augmented generation and traceable answersUnderstand retrieval, ranking, context assembly, citations, and failure strategies in Retrieval-Augmented Generation.
   2. Agent handoff: tools, memory, and boundariesUse a support case to understand function tools, user preferences, session state, and why handoff is not the same as a real live transfer.
   3. Multimodal apps: text, images, audio, and async workflowsUnderstand input governance, asynchronous state, and cost controls for vision, speech, image generation, and video tasks.
   4. Related lab
   Open track
2. 06

   Data, evaluation, and MLOps

   ML

   Separate regression, classification, evaluation, drift, and deployment pipelines; Azure ML is supplementary, not complete AZ exam coverage.

   Extended learning · 3 lessons · 0/3

   Lessons and context

   1. Data splits and baselines: machine learning starts with problem definitionSeparate train, validation, test, leakage, and simple baselines so you do not over-interpret experiments.
   2. Classification evaluation: tradeoffs beyond accuracyUnderstand confusion matrices, precision, recall, thresholds, and business cost.
   3. MLOps monitoring: from a model file to a maintainable systemUnderstand model registration, deployment, drift, post-deployment review, and human review.
   4. Related lab
   Open track

Certification routes

Certification objectives are learning lenses inside Learn, not a separate top-level section.