Progress

Learning progress and next step

Choose one next lesson from your unfinished work. Progress is stored in this browser only and is shared with course pages.

0/18 lessons complete

Next: VM basics: responsibility boundaries, identity, and observability

From virtual machines to serverless

Compute

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

0/3

  1. VM basics: responsibility boundaries, identity, and observability
  2. Container Apps: revisions, scaling, and a smaller platform surface
  3. Serverless operations: triggers, cold starts, and cost guardrails
Practice lab

Objects, files, and recovery

Storage

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

0/3

  1. Blob lifecycle: tiers, access, and retention
  2. Azure Files: shared files and identity boundaries
  3. Backup and continuity: work backward from recovery targets
Practice lab

Segmentation, private access, and edge

Network

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

0/3

  1. VNet segmentation: subnets, routes, and minimal exposure
  2. Private Link and DNS: private access is more than one endpoint
  3. Edge and hybrid networking: Front Door, ExpressRoute, and failure domains
Practice lab

Relational, document, and search

Database

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

0/3

  1. Azure SQL: day-to-day design for a managed relational database
  2. Cosmos DB: partitioning, consistency, and throughput
  3. Azure AI Search: indexes, vectors, and citations
Practice lab

RAG, agents, and multimodal input

AI

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

0/3

  1. RAG: retrieval-augmented generation and traceable answers
  2. Agent handoff: tools, memory, and boundaries
  3. Multimodal apps: text, images, audio, and async workflows
Practice lab

Data, evaluation, and MLOps

ML

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

0/3

  1. Data splits and baselines: machine learning starts with problem definition
  2. Classification evaluation: tradeoffs beyond accuracy
  3. MLOps monitoring: from a model file to a maintainable system
Practice lab

Suggested sequence, not certification prerequisites

  1. Cloud foundations — Compute, storage, network, and database.
  2. Applications and models — RAG, multimodal input, data preparation, evaluation, and MLOps.
  3. Architecture review — Review official certification gaps, projects, and lab evidence.

Security, governance, monitoring, cost, and continuity apply throughout. Completing notes does not establish exam readiness.