As a Microsoft Azure AI engineer, you build, manage, and deploy AI solutions that leverage Azure AI. Your responsibilities include participating in all phases of AI solutions development, including: Requirements definition and design Development Deployment Integration Maintenance Performance tuning Monitoring You work with solution architects to translate their vision. You also work with data scientists, data engineers, Internet of Things (IoT) specialists, infrastructure administrators, and other software developers to: Build complete and secure end-to-end AI solutions. Integrate AI capabilities in other applications and solutions. As an Azure AI engineer, you have experience developing solutions that use languages such as: Python C# You should be able to use Representational State Transfer (REST) APIs and SDKs to build secure image processing, video processing, natural language processing, knowledge mining, and generative AI solutions on Azure. You should: Understand the components that make up the Azure AI portfolio and the available data storage options. Be able to apply responsible AI principles. Important The English language version of this certification was updated on February 7, 2024. Review the study guide linked on the Exam AI-102 page for details about recent changes.

Mastering the basics in AI can help you jump-start your career and get ready to dive deeper into the other technical opportunities Azure offers. AI opens doors into possibilities that might have seemed like science fiction only yesterday. Using AI, you can build solutions, improve your apps, and advance technology in many fields, including healthcare, financial management, and environmental protection, to name just a few. The Microsoft Certified: Azure AI Fundamentals certification could be a great fit for you if you'd like to: Prove that you have the AI skills it takes to build a better world. Earning your Azure AI Fundamentals certification can supply the foundation you need to build your career and demonstrate your knowledge of common AI and machine learning workloads—and what Azure services can solve for them. Validate your foundational knowledge of machine learning and AI concepts, along with related Azure services. Data science and software engineering experience are not required. However, you would benefit from having awareness of: Basic cloud concepts Client-server applications You can use Azure AI fundamentals to reinforce your basics for other Azure role-based certifications, like Azure Data Scientist Associate, Azure AI Engineer Associate, or Azure Developer Associate, but it’s not a prerequisite for any of them. To prepare for the exam, we recommend that you: Fully understand the skills measured. Study the relevant self-paced content on Microsoft Learn, attend a Microsoft Azure Virtual Training Day: AI Fundamentals, or sign up for an instructor-led training event with a Training Services Partner. Take the free Practice Assessment to validate your knowledge. Get a trial subscription and give it a try. Check out Master the basics with the Azure AI Fundamentals certification to learn more about this certification and how to get ready. What's next? Register for your exam! After you pass the exam and earn your certification, celebrate your certification badge and skills on social platforms such as LinkedIn. To find out more, visit use and share certification badges. Depending on your goals, you may choose to master the basics with the Azure Data Fundamentals certification, level up with the Azure AI Engineer Associate, Azure Data Scientist Associate, or Azure Developer Associate certifications, or find the right Microsoft Azure certification for you, based on your profession (or the one you aspire to). Important The English language version of this certification was updated on January 31, 2024. Review the study guide linked on the Exam AI-900 page for details about recent changes.

As a candidate for this certification, you should have subject matter expertise in integrating, transforming, and consolidating data from various structured, unstructured, and streaming data systems into a suitable schema for building analytics solutions. As an Azure data engineer, you help stakeholders understand the data through exploration, and build and maintain secure and compliant data processing pipelines by using different tools and techniques. You use various Azure data services and frameworks to store and produce cleansed and enhanced datasets for analysis. This data store can be designed with different architecture patterns based on business requirements, including: Modern data warehouse (MDW) Big data Lakehouse architecture As an Azure data engineer, you also help to ensure that the operationalization of data pipelines and data stores are high-performing, efficient, organized, and reliable, given a set of business requirements and constraints. You help to identify and troubleshoot operational and data quality issues. You also design, implement, monitor, and optimize data platforms to meet the data pipelines. As a candidate for this certification, you must have solid knowledge of data processing languages, including: SQL Python Scala You need to understand parallel processing and data architecture patterns. You should be proficient in using the following to create data processing solutions: Azure Data Factory Azure Synapse Analytics Azure Stream Analytics Azure Event Hubs Azure Data Lake Storage Azure Databricks Important The English language version of this certification was updated on November 2, 2023. Review the study guide linked on the Exam DP-203 page for details about recent changes.

As a candidate for this exam, you should have subject matter expertise in applying data science and machine learning to implement and run machine learning workloads on Azure. 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. As a candidate for this exam, you should have knowledge and experience in data science by using: Azure Machine Learning MLflow Important The English language version of this certification was updated on October 18, 2023. Review the study guide linked on the Exam DP-100 page for details about recent changes.

Warning This certification will retire on April 30, 2024. You will no longer be able to earn this certification after this date. Certification renewal will be available for at least six months after the certification retires. Read the blog post. As a candidate for the Azure Enterprise Data Analyst Associate certification, you should have subject matter expertise in designing, creating, and deploying enterprise-scale data analytics solutions. Your responsibilities for this role include performing advanced data analytics at scale, such as: Cleaning and transforming data. Designing and building enterprise data models. Incorporating advanced analytics capabilities. Integrating with IT infrastructure. Applying development lifecycle practices. As a professional in this role, you: Help collect enterprise-level requirements for data analytics solutions that include Azure and Power BI. Advise on data governance and configuration settings for Power BI administration. Monitor data usage. Optimize performance of the data analytics solutions. As an Azure enterprise data analyst, you collaborate with other roles, such as: Solution architects Data engineers Data scientists AI engineers Database administrators Power BI data analysts As a candidate for this certification, you should have advanced Power BI skills, including managing data repositories and data processing in the cloud and on-premises, along with using Power Query and Data Analysis Expressions (DAX). You should also be proficient in consuming data from Azure Synapse Analytics and should have experience querying relational databases, analyzing data by using Transact-SQL (T-SQL), and visualizing data. Important The English language version of this certification was updated on January 30, 2024. Review the study guide linked on the Exam PL-200 page for details about recent changes.

As a candidate for this certification, you implement solutions that provide insights into customer profiles and that track engagement activities to help: Improve customer experiences. Increase customer retention. You should have firsthand experience with: Dynamics 365 Customer Insights - Data and one or more additional Dynamics 365 apps Microsoft Power Query Microsoft Dataverse Common Data Model Microsoft Power Platform You should also have direct experience with practices related to: Privacy Compliance Consent Security Responsible AI Data retention policy As a candidate for this exam, you need experience with processes related to key performance indicators (KPIs), data retention, validation, visualization, preparation, matching, fragmentation, segmentation, and enhancement. You should have a general understanding of: Azure Machine Learning Azure Synapse Analytics Azure Data Factory If you need more experience before you try to earn this certification, here are some suggestions: Work as a Microsoft Dynamics 365 consultant on Customer Insights projects. Join the Dynamics 365 community. You don't have to be a certificate holder. Take available learning paths on Microsoft Learn and complete more hands-on practice. Sign up for an instructor-led training course. Read the Discover your career path blog for more information. Important The English language version of this certification was updated on October 19, 2023. Review the study guide linked on the Exam MB-260 page for details about latest changes.

As a candidate for this certification, you should have subject matter expertise in designing, creating, and deploying enterprise-scale data analytics solutions. Your responsibilities for this role include transforming data into reusable analytics assets by using Microsoft Fabric components, such as: Lakehouses Data warehouses Notebooks Dataflows Data pipelines Semantic models Reports You implement analytics best practices in Fabric, including version control and deployment. To implement solutions as a Fabric analytics engineer, you partner with other roles, such as: Solution architects Data engineers Data scientists AI engineers Database administrators Power BI data analysts In addition to in-depth work with the Fabric platform, you need experience with: Data modeling Data transformation Git-based source control Exploratory analytics Languages, including Structured Query Language (SQL), Data Analysis Expressions (DAX), and PySpark