How to Pass AI-103: A 3-Week Study Plan for Microsoft’s New Azure AI Exam
AI-103 is new enough (beta April 2026, generally available around June) that most prep advice for it doesn’t exist yet — which is actually your advantage. The skills outline is public, the domain weights are published, and almost nobody else studying has a structured plan. Here’s one, built directly from the exam’s blueprint.
What you’re up against
| Domain | Weight |
|---|---|
| Generative AI and agentic solutions | 30–35% |
| Plan and manage an Azure AI solution | 25–30% |
| Computer vision solutions | 10–15% |
| Text analysis solutions | 10–15% |
| Information extraction | 10–15% |
Passing score is 700/1000, the exam costs $165, and the credential (Azure AI Apps and Agents Developer Associate) renews annually through a free online assessment. The language of the exam is Python, and the platform at its center is Microsoft Foundry. Read the weights like a study budget: roughly two-thirds of your points live in the top two domains, so that’s where two of your three weeks go.
Week 1 — Foundations and the “plan and manage” domain
- Set up a real environment: an Azure subscription and a Microsoft Foundry project. Every hour spent hands-on beats two hours of videos.
- Work the 25–30% domain: choosing and provisioning the right Azure AI services, securing them, responsible AI practices, monitoring, and cost management.
- End the week with a free AI-103 sample set to baseline yourself — score honestly, note the domains that hurt.
Week 2 — The big one: generative AI and agents
- This is a third of your exam. Build, don’t just read: a small generative-AI app in Foundry — model selection, grounding with your own data, orchestration, deployment.
- Then make it agentic: tool use, multi-step tasks, the agent patterns Foundry supports. If you can explain when an agent beats a plain completion call, you’re thinking like the exam.
- Fold in the two language domains (text analysis, information extraction) late in the week — they’re smaller, and they reuse concepts you’ll have just practiced.
Week 3 — Vision, mocks, and readiness
- Close the computer-vision domain (10–15%): image analysis and custom vision scenarios.
- Shift to timed mock exams and let a readiness score tell you when you’re consistently clearing the bar — our AI-103 practice platform tracks this per domain.
- Final days: re-drill only your weakest domain. Resist the urge to re-study what you already pass.
One honest note on question sources
Because AI-103 is new, “dumps” sites are already selling recycled AI-102 questions with the labels swapped — the two exams measure different things, so that’s worse than studying nothing. Every question in our AI-103 bank is original and written from the published skills outline. Use the study guide as your domain map, and see AI-102 vs AI-103 if you’re still deciding which exam to sit.
Frequently asked questions
How long does it take to prepare for AI-103?
With existing Azure and Python experience, about three focused weeks. Starting without Azure background, plan 6–8 weeks and build fundamentals first.
Is AI-103 hard?
It's an associate-level exam with real hands-on expectations — Python, Microsoft Foundry, and agent patterns. The 30–35% generative AI and agents domain decides most pass/fail outcomes.
Do I need AI-901 before AI-103?
There's no formal prerequisite. But AI-103 assumes working comfort with Azure AI concepts, so fundamentals-level knowledge is effectively expected even though it isn't required.
Are there official Microsoft practice tests for AI-103?
Microsoft's official practice assessments typically arrive within a couple of months of an exam going GA. Our free AI-103 question bank is available now, written from the published skills outline.
What should I study most for AI-103?
Generative AI and agentic solutions (30–35%) plus planning and managing Azure AI solutions (25–30%) — together roughly two-thirds of the exam. Budget your study time accordingly.
