As a candidate for this certification, you should have subject matter expertise in applying data science and machine learning to implement and run machine learning workloads on Azure. Additionally, you should have knowledge of optimizing language models for AI applications using Azure AI. Your responsibilities for this role include: Designing and creating a suitable working environment for data science workloads. Exploring data. Training machine learning models. Implementing pipelines. Running jobs to prepare for production. Managing, deploying, and monitoring scalable machine learning solutions. Using language models for building AI applications. As a candidate for this certification, you should have knowledge and experience in data science by using: Azure Machine Learning MLflow Azure AI services, including Azure AI Search Azure AI Foundry Important Azure AI Foundry is now Microsoft Foundry. We are in the process of updating the exam and all associated materials.

As a candidate for this Microsoft Certification, you’re at the beginning of your career in AI solution development. For this exam, you should have conceptual knowledge of AI solutions in Azure and the foundational technical skills to work with them. You also need knowledge of Python coding syntax and programming techniques, and you should be familiar with Azure resources. Note The bullets that follow each of the skills measured are intended to illustrate how we are assessing that skill. Related topics may be covered in the exam. Note Most questions cover features that are general availability (GA). The exam may contain questions on Preview features if those features are commonly used. You should be familiar with REST APIs, SDKs, and CLIs. Important The English language version of this certification was updated on April 15, 2026. Review the study guide linked on the Exam AI-900 and Exam AI-901 pages for details about recent changes.

To earn this Microsoft Applied Skills credential, learners demonstrate the ability to implement a data science solution by using Microsoft Fabric, including: Ingesting, loading, exploring, and preparing data Training, tracking, and scoring a model Candidates for this credential should be familiar with data science and AI fundamentals, in addition to open-source frameworks, such as scikit-learn and SynapseML. They should also have experience with: Python MLflow Synapse Data Science in Microsoft Fabric