Azure Mastery
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Azure Mastery
Microsoft Certification AI-300
On-device AI scores your readiness, builds an adaptive study plan, and flags topics fading from memory — before they cost you the exam.
Get exam-ready for AI-300 (Microsoft ML Operations Engineer) on iPhone or iPad. Azure Mastery uses on-device AI to predict your readiness score across all five AI-300 domains, build a personalised study plan from your weak spots, and surface topics you're forgetting. Core study stays on-device and works offline; optional sync uses your private iCloud account.
The exam
AI-300 is the Microsoft Certified: ML Operations Engineer Associate exam — the credential hiring managers expect when posting "MLOps Engineer", "GenAIOps Engineer", "AI Platform Operations", or "ML Reliability Engineer" roles. AI-300 covers the operational lifecycle of ML and generative-AI systems: MLOps infrastructure, model lifecycle, GenAIOps observability, and performance tuning at scale.
AI-300 is hands-on and lifecycle-focused. It validates that you can design and implement an MLOps infrastructure (CI/CD for ML, environment management, registries, governance), implement machine learning model lifecycle and operations (training pipelines, deployment, monitoring, retraining), design and implement a GenAIOps infrastructure (prompt versioning, evaluation pipelines, deployment patterns for LLM apps), implement generative AI quality assurance and observability (groundedness checks, drift, hallucination detection, telemetry), and optimise generative AI systems and model performance (caching, batching, model selection, fine-tuning). Expect scenario questions that span CI/CD configs, monitoring strategies, and trade-off reasoning.
Microsoft updated the AI-300 skills outline in March 2026. Every question in Azure Mastery's AI-300 bank is mapped to the current outline — no leftover questions on retired services. Read the official outline at learn.microsoft.com.
Skills measured · April 2026
Five domains, with weights set by Microsoft's March 2026 update. Every domain summary below is paraphrased from the official skills outline; bullet-level objectives in Azure Mastery are tagged so you always know which domain you're being tested on and where your weak spots cluster.
The platform layer. CI/CD for ML — Azure DevOps and GitHub Actions for model build, test, deploy. Environment management (Conda, Docker), feature stores, model registries, governance and approval gates. Plus reproducibility, lineage tracking, and Responsible-AI governance baseline. Around 6–12 questions per sitting.
Largest domain. Training pipelines, automated training (AutoML), deployment to managed online and batch endpoints, A/B traffic splitting, blue/green and canary deployments. Monitoring — data drift, model drift, performance metrics. Retraining triggers and automated rollback. Plus model packaging, containerisation, and Kubernetes-attached compute scenarios. Around 10–18 questions.
The generative-AI ops layer. Prompt versioning and prompt registries, evaluation pipelines (groundedness, relevance, fluency, similarity), deployment patterns for LLM apps (Foundry-deployed apps, Azure Functions, Container Apps), feature-flag-driven prompt rollouts, and observability for token usage and cost. Around 8–15 questions.
Quality at the production edge. Groundedness checks, hallucination detection, content-safety telemetry, drift detection on prompt or RAG inputs, regression suites for prompts. Distributed tracing across LLM calls and agent invocations. Around 4–9 questions.
Performance and cost. Caching strategies (semantic cache, embedding cache), batching, model selection (small models for routing, large for generation), fine-tuning vs few-shot vs RAG trade-offs, token budget management, capacity planning for token throughput. Around 4–9 questions.
Designed for AI-300
Azure Mastery ships with 400 AI-300 practice questions, every one written specifically against the current (March 2026) skills outline. Each question carries a domain tag mapped to the official five domains (MLOps infra, ML lifecycle, GenAIOps, GenAI QA, optimization), so you always know which area you're being tested on and where your weak spots are clustered. CI/CD pipeline YAML, MLflow tracking configs, prompt-evaluation scripts, and observability setups appear throughout — matching the format of the live exam.
The on-device Exam IQ engine predicts your AI-300 score before you sit the exam. After roughly 30 questions it has enough signal to give a confidence-scored prediction (e.g. "786 ±37, 68% confidence") — and tells you the specific topics that are dragging your readiness down. No vague "study more" advice; just a ranked list of objectives where improvement would move your score the furthest.
The adaptive study plan rebuilds itself from your answer history. Get a scenario question wrong? The engine surfaces another question in the same domain in your next session. Master a topic across three sessions and it backs off, prioritising the next-highest-leverage gap. The plan optimises for the gap between where you are and the 700 pass score, not for blind volume.
Knowledge decay tracking matters more for AI-300 than for foundational exams — five domains is a lot to retain, and the topic you mastered three weeks into your study window is the topic you'll forget by exam day if you stop revising. Azure Mastery tracks every topic's decay curve and flags topics approaching expiry. The padlock icon on the Today screen is your "revisit before you forget" cue, and weak-spot drills automatically pull from decayed topics first.
Real exam simulation mode runs at AI-300's actual length and time pressure: a randomised 40–60-question set drawn from the full 400-question bank, weighted by domain percentages from the April 2026 outline, with the 100-minute timer running and no jumping back to flag-and-review. It's the closest you can get to the live Pearson VUE / online-proctored experience without sitting the exam.
Answer Coach turns each missed answer into a private, grounded lesson: the misconception, key distinction, and rule to remember. It always uses authored certification guidance; on supported devices, an optional on-device model may rewrite the note only when it passes grounding checks.
During your first week, Aura adapts the next step as you go. Every session ends with a concise recap of what changed, what to focus on, and the best follow-up.
Everything essential runs on-device. Your answer history, readiness gauge, and coaching stay private. Optional sync uses your private iCloud account; there is no Azure Mastery account, tracking, or external processing server.
6-week study plan
Most candidates pass AI-300 after four to eight weeks of focused study, depending on prior Azure experience. The six-week plan below maps onto the five AI-300 domains, Azure Mastery's adaptive sessions, and the in-app exam simulator. Adjust pace to taste — the readiness gauge tells you when you're done, not the calendar.
Inside the app
AI-300's question bank uses the same formats Microsoft puts on the live exam — not just multiple choice. Each visualisation below is a faithful mock of how the type renders inside Azure Mastery on iPhone and iPad. Exam-simulator mode runs all of them at full 100-minute length with no flag-and-review jumps, mirroring Pearson VUE.
A real AI-300 question-bank example with one correct answer. The app explains every option after you answer.
Exam-specific sampleA real AI-300 multi-select item. Every required selection must be correct to earn the mark.
All-or-nothingA real AI-300 interactive-format prompt, rendered for touch on iPhone and iPad.
Interactive itemA real AI-300 prompt that tests recognition inside a visual or contextual interface.
Tap targetA real AI-300 case-study scenario with linked questions that share the same requirements and environment.
Multi-questionAnswer Coach uses the bank's authored rationale to explain the misconception, key distinction, and rule to remember. On supported devices, an optional on-device model may rewrite the note only when it passes grounding checks.
App exclusiveFrequently asked
The AI-300 voucher is USD $165 in the United States. Pricing varies by region — in the UK it's typically around £128. Microsoft sometimes runs free-voucher promotions during events such as Microsoft Build or Microsoft Ignite, so check your Microsoft Learn profile for any active offers before booking. AI-300 also requires annual renewal (free, online), so factor that into long-term cost planning.
Yes. Microsoft Associate certifications including AI-300 expire annually. Renewal is free — a 25–30 question online assessment on Microsoft Learn within the six-month window before your expiration date. The renewal targets recent skills outline updates, so staying current is straightforward if you remain broadly active in the role. (Fundamentals certifications such as AZ-900 are different — those don't expire.)
The first retake is allowed after 24 hours. Second and third retakes each require a 14-day wait. Microsoft caps retakes at five attempts per 12-month rolling period. Each attempt requires a new voucher purchase.
Allow four to eight weeks of focused study if you already work with Azure Machine Learning or generative AI systems. Spend longer if MLOps, GenAIOps, deployment infrastructure, observability, quality evaluation, or model lifecycle automation are new to you. Azure Mastery's readiness gauge helps you decide when to schedule the exam.
Yes. Microsoft retired DP-100 and the Azure Data Scientist Associate certification on 1 June 2026 and identifies AI-300, Machine Learning Operations Engineer Associate, as its replacement. The emphasis changed: DP-100 focused on data-science model development, while AI-300 focuses on operationalizing machine learning and generative AI through MLOps, GenAIOps, observability, quality, and lifecycle automation.
AI-200 first. AI-200 (AI Cloud Developer Associate) builds the cloud platform that AI workloads run on — compute, vector databases, integration, security. AI-300 (ML Operations Engineer Associate) operates ML and generative-AI systems on that platform. AI-200 is upstream of AI-300 in most roles.
Where AI-300 fits
AI-300 is the Microsoft Machine Learning Operations Engineer Associate certification. It replaced the retired DP-100 route and pairs with AI-200 for engineers who build the surrounding Azure AI platform as well as operate models and generative AI systems.
Download Azure Mastery free. 400 AI-300 practice questions across all five domains, AI score prediction, full-length exam simulator, adaptive study plan. iPhone & iPad.
Download Azure Mastery — free iPhone & iPad · Free to start · No account required