Prepare for AI-900 with a blueprint-led plan

Certification overview

AI-900 is offered by Microsoft. Builds vocabulary for discussing machine learning, computer vision, NLP, generative AI, and responsible AI on Azure. This guide follows Skills measured as of May 2, 2025; always confirm provider changes before scheduling.

Who should pursue it

Technical and non-technical professionals building foundational knowledge of AI workloads and Azure AI services.

• You want a conceptual introduction to AI and machine learning workloads.
• You need to recognize where common Azure AI services fit.
• You want to practice responsible-AI concepts alongside technical fundamentals.

Typical job roles

AI-aware business, product, project, cloud, data, and entry-level AI solution roles.

Skills measured

The current public standard organizes preparation into 5 domains. Each domain should be studied in the context of its linked objectives rather than as an isolated topic list.

• Describe Artificial Intelligence workloads and considerations (19% of the published blueprint): Identify common AI workloads and apply Microsoft's guiding principles for responsible AI. Official range: 15-20%.
• Describe fundamental principles of machine learning on Azure (19% of the published blueprint): Identify machine learning techniques and concepts and describe Azure Machine Learning capabilities. Official range: 15-20%.
• Describe features of computer vision workloads on Azure (19% of the published blueprint): Identify common computer vision solutions and the Azure services that support them. Official range: 15-20%.
• Describe features of Natural Language Processing (NLP) workloads on Azure (19% of the published blueprint): Identify common NLP scenarios and the Azure services that support them. Official range: 15-20%.
• Describe features of generative AI workloads on Azure (24% of the published blueprint): Identify generative AI solution features and describe Microsoft Azure generative AI services. Official range: 20-25%.

Official exam structure

The public profile lists Provider did not publish a fixed item count questions and 45 minutes. Supported preparation formats include Multiple Choice, Multiple Response, Scenario Decision, Drag and Drop. The provider's delivery and scoring rules remain authoritative.

Recommended experience

Basic cloud and client-server concepts are useful; deep data-science experience is not required.

Common candidate mistakes

The most avoidable errors are using an outdated objective list, over-studying familiar domains, memorizing practice wording, skipping practical work, and waiting until the final week to test timing.

Study strategy

Begin with a mixed diagnostic, map every miss to an objective, and rotate through focused study blocks. Combine source reading with labs, scenarios, or work artifacts where the blueprint expects applied judgment.

Time management

Practice within the 45-minute limit without forcing an identical pace on every item. Use a steady first pass, flag questions that warrant deeper analysis, and protect a final review window.

Practice exam strategy

Keep explanations on during targeted study and off during full simulation. Review incorrect answers, uncertain correct answers, domain balance, and pacing before deciding the next study action.

Exam-day strategy

Verify identification and delivery rules with the provider, arrive or check in early, read each prompt for the requested decision, and recover quickly after difficult items. Do not let one question consume the time needed for the rest of the exam.