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  1. Programs
  2. Microsoft Certified: Azure AI Fundamentals

Microsoft Certified: Azure AI Fundamentals

Microsoft

Certification

Become a contributor for free to openly demonstrate student outcomes, industry alignment & eligibility criteria.

Demonstrate fundamental AI concepts related to the development of software and services of Microsoft Azure to create AI solutions.

Cost

$99Show moreShow less

Format

Hybrid

Eligibility Calculator

Which aid programs apply to this program?

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Program Pathways

Credentials this program stacks toward

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Program Details

Detailed information about this program

This certification is an opportunity for you to demonstrate knowledge of machine learning and AI concepts and related Microsoft Azure services. As a candidate for this certification, you should have familiarity with the self-paced or instructor-led learning material. This certification is intended for you if you have both technical and non-technical backgrounds. 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 prepare for other Azure role-based certifications like Azure Data Scientist Associate or Azure AI Engineer Associate, but it’s not a prerequisite for any of them. You may be eligible for ACE college credit if you pass this certification. You will have 45 minutes to complete this assessment. Exam policy This exam will be proctored. You may have interactive components to complete as part of this exam. To learn more about exam duration and experience, visit: Exam duration and exam experience. If you fail a certification exam, don’t worry. You can retake it 24 hours after the first attempt. For subsequent retakes, the amount of time varies. For full details, visit: Exam retake policy. Assessed on this exam - Describe Artificial Intelligence workloads and considerations - Describe fundamental principles of machine learning on Azure - Describe features of computer vision workloads on Azure - Describe features of Natural Language Processing (NLP) workloads on Azure - Describe features of generative AI workloads on Azure Need accommodations? We offer a variety of accommodations to support you. This exam is offered in the following languages: English, Japanese, Chinese (Simplified), Korean, German, French, Spanish, Portuguese (Brazil), Russian, Indonesian (Indonesia), Arabic (Saudi Arabia), Chinese (Traditional), Italian

Requirements

What you need to earn this credential

No requirements listed.

Financial Aid

Eligible funding programs

No funding information available.

Scholarships

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Locations

Where this program is offered

No locations specified.

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Related Programs

Programs related to this one

No related programs.

Skills & Competencies

Skills developed through this program

  • Describe Artificial Intelligence workloads and considerations
  • Describe fundamental principles of machine learning on Azure
  • Describe features of computer vision workloads on Azure
  • Describe features of Natural Language Processing (NLP) workloads on Azure
  • Describe features of generative AI workloads on Azure
  • Identify features of common AI workloads
Career Pathways

Occupations this program prepares you for

  • Computer Systems Analysts15-1211.00
What You'll Learn

Key competencies developed through this program

Auto-populated·from NSX Competency Framework

Mastery: developing (Level 2)(based on Certification)

  • Program and system malfunctions — diagnose and resolve common failures using established troubleshooting procedures with minimal oversight in a business IT environment.
  • Staff and end-user assistance — provide routine technical support and guidance for computer-related problems, adapting explanations to varying user skill levels.
  • System monitoring and maintenance — coordinate scheduled testing and update installations for computer programs across departmental systems with reduced supervision.
  • Business problem analysis — apply spreadsheet and database tools to develop basic cost analysis or inventory control solutions for familiar operational scenarios.
  • Cross-system integration — link departmental computer systems by configuring compatible data-sharing settings following established integration guidelines.
  • Client-server and web applications — develop and maintain functional application components using object-oriented languages and standard web platform development software.
  • Data modeling and requirements documentation — conduct structured interviews and produce data flow diagrams to capture system requirements for mid-complexity projects.
  • Management consultation — present system design options and trade-offs to supervisors, supporting agreement on principles through clearly written proposals.
  • Requirements analysis software — use architecture and analysis tools to validate that proposed solutions align with documented business and technical requirements.
  • Time and task coordination — manage personal workload across concurrent system projects, meeting deadlines through proactive scheduling and progress tracking.

Some details on this page are auto-populated from public workforce data sources: O*NET (opens in new tab), BLS (opens in new tab), College Scorecard (opens in new tab), DOL Training Provider Results (opens in new tab), NSX (opens in new tab). Provided in partnership with LER.me Career Intelligence.

Student Outcomes

Performance metrics for this program

Completion Rate
Not reported
Placement Rate
Not reported