Microsoft Azure
Azure administration & cert prep — AZ-104 decks, practice exams, and hands-on IaC/CLI/Azurite labs.
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AZ-104 · Manage Identities & Governance36 cards — step 1 of 10Then get hands-on: Azure IaC lab — 7 scenarios.
AZ-104 Azure Administrator — learning pathExam-domain map, decks, practice exams, free hands-on IaC/CLI/Azurite labs, a skill map, and free-first resources — one workspace.AI-300 Machine Learning Operations Engineer — learning pathAzure MLOps/GenAIOps (Azure ML + Foundry) — five skill-area decks (99 cards), an 87-question scenario practice exam, a study routine, and free-first resources, verified against the official skills-measured guide.
Flashcard decks
10 decks · 285 cards · in study orderAZ-104 Administrator
5 decks · 186 cards1AZ-104 · Manage Identities & GovernanceExam domain 1 (20–25%): Microsoft Entra ID, users & groups, Azure RBAC, Azure Policy, subscriptions & management groups, tags, resource locks, and cost management.362AZ-104 · Implement & Manage StorageExam domain 2 (15–20%): storage accounts & redundancy (LRS/ZRS/GRS/GZRS), blob tiers & lifecycle, access control (keys, SAS, stored access policies, Entra RBAC), storage firewalls, service vs private endpoints, encryption, Azure Files & File Sync, AzCopy & Storage Explorer.383AZ-104 · Deploy & Manage ComputeExam domain 3 (20–25%): ARM templates & Bicep (deployment modes, parameters, export, what-if), virtual machines (sizes, managed disks, encryption, availability sets vs zones, scale sets), containers (ACR, ACI, Container Apps), and Azure App Service (plans, scaling, deployment slots, TLS, networking, backup).374AZ-104 · Implement & Manage Virtual NetworkingExam domain 4 (15–20%): virtual networks & subnets, VNet peering, public/private IPs, user-defined routes, NSGs & ASGs, effective security rules, Azure Bastion, service vs private endpoints, Azure DNS & private DNS, load balancers, and Network Watcher troubleshooting.385AZ-104 · Monitor & Maintain ResourcesExam domain 5 (10–15%): Azure Monitor (metrics vs logs, Log Analytics, diagnostic settings, activity log, KQL, alerts, action groups, alert processing rules, Insights, the Azure Monitor Agent & DCRs), Network Watcher, and backup/recovery (Azure Backup, Recovery Services vs Backup vaults, backup policies, restore, Azure Site Recovery, RPO/RTO).37
AI-300 MLOps
5 decks · 99 cards6AI-300 · ML Model Lifecycle & Operations (25-30%)The biggest area — running the model lifecycle in Azure Machine Learning: workspaces & assets, data/datastores, environments, training as jobs, pipelines & components, the model registry, endpoints (managed online + batch), deployments, and the automation (schedules, event triggers, retraining) that turns a notebook experiment into an operated system.287AI-300 · GenAIOps Infrastructure (20-25%)Standing up generative-AI systems in Microsoft Foundry (Azure AI Foundry): projects & hubs, the model catalog, deploying and fine-tuning models, prompt flow for orchestration, connections & Azure OpenAI, RAG with grounding data (Azure AI Search), agents, and deploying GenAI apps to endpoints — the operational plumbing behind an LLM app, not prompt-writing.238AI-300 · MLOps Infrastructure — IaC & CI/CD (15-20%)The platform plumbing: provisioning workspaces, compute, and endpoints as infrastructure-as-code (Bicep / ARM / Terraform / Azure CLI), building CI/CD for ML with GitHub Actions or Azure DevOps (environments, approvals, staged promotion), securing it with RBAC, managed identity, VNets and Key Vault, and organizing dev/test/prod — the DevOps discipline the 'operations engineer' title is really about.189AI-300 · GenAI Quality Assurance & Observability (10-15%)Making generative-AI output trustworthy and watchable: evaluating quality with metrics (groundedness, relevance, coherence, fluency, similarity) and AI-assisted + safety evaluators, content safety / responsible-AI filtering, monitoring deployed GenAI apps (Application Insights, token usage, latency, feedback), and catching regressions before and after release.1510AI-300 · Optimize GenAI Systems & Performance (10-15%)Tuning deployed systems for cost, latency, and quality: token/cost optimization, caching, model right-sizing, throughput (PTU vs pay-go, provisioned capacity, batching), retrieval-quality tuning for RAG, prompt optimization, and scaling endpoints — squeezing a production GenAI/ML system without a full rebuild.15
Practice exams
2 setsPractice ExamA full-length, real-format AZ-104 practice exam. Each attempt draws 45 questions at random from a large pool spanning all five exam domains (Identity & Governance, Storage, Compute, Networking, Monitor), weighted like the official blueprint. Scenario-style multiple choice with a worked explanation on every question. Pass mark is 70% — the real 700/1000 exam bar. Progress saves in your browser; no login needed.AI-300 Practice ExamA scenario-style practice exam for Microsoft AI-300 (Operationalizing Machine Learning and Generative AI Solutions). Draws 50 questions per attempt from a blueprint-weighted pool (Model Lifecycle 25-30% · GenAIOps Infrastructure 20-25% · MLOps Infrastructure 15-20% · GenAI QA & Observability 10-15% · Optimize 10-15%), scored at 70% to mirror the real 700/1000 cut, with a 120-minute soft timer. Original 5-choice 'which Azure service fits' MCQs with worked explanations and a per-area weak-area breakdown. Pairs with the five AI-300 decks. Progress saves in your browser — no login.