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 AI Search AI Services Important The English language version of this certification will be updated on April 11, 2025. Review the study guide linked in the Certification resources section for details about upcoming changes.

To earn this Microsoft Applied Skills credential, learners demonstrate the ability to train and manage machine learning models with Azure Machine Learning. Candidates for this credential should be familiar with Azure services and should have experience with Azure Machine Learning and Mlflow. Candidates should also have experience performing tasks related to machine learning by using Python.

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