Historical AI-900 Scope
AI-900 was Microsoft's Microsoft Azure AI Fundamentals exam until its retirement on 2026-06-30. Its final published scope covered 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. This page preserves that dated outline for historical comparison and curriculum cleanup. It is not a schedulable exam, and its prices, delivery details, registration links, and practice calls to action must not be used for a new certification plan.
Who AI-900 Was For
Nobody should start a new certification plan around AI-900. This page serves former candidates, credential holders, training teams removing stale material, and practitioners deciding whether prior preparation is reusable for AI-901. New candidates should verify the active Microsoft page and current dated study guide before booking.
Skills You Should Be Ready to Demonstrate
- Preserve practical experience from Describe Artificial Intelligence workloads and considerations only where the current guide shows meaningful overlap
- Preserve practical experience from Describe fundamental principles of machine learning on Azure only where the current guide shows meaningful overlap
- Preserve practical experience from Describe features of computer vision workloads on Azure only where the current guide shows meaningful overlap
- Preserve practical experience from Describe features of Natural Language Processing (NLP) workloads on Azure only where the current guide shows meaningful overlap
- Preserve practical experience from Describe features of generative AI workloads on Azure only where the current guide shows meaningful overlap
How to Reuse Your Preparation
Do not rename an old AI-900 checklist or question bank as AI-901. Compare the two official guides objective by objective and classify each historical skill as transferable, changed, or absent. Use transferable experience as a diagnostic baseline, then rebuild labs and review around AI-901's current domain weights, products, role expectations, security controls, and operational tasks. Because Microsoft identifies AI-901 as the direct replacement, review the skill map below before assuming equivalence.
What changed from AI-900 to AI-901
| Historical AI-900 | Current AI-901 | Practical impact |
|---|
AI workloads and considerations
15-20% | Identify AI concepts and capabilities
40-45% | AI-901 consolidates conceptual coverage and gives it substantially more weight, including responsible AI and choosing capabilities for a scenario. |
Machine learning, vision, and NLP fundamentals
45-60% combined | Identify AI concepts and capabilities
40-45% | The foundational concepts still transfer, but they are organized as one integrated decision-oriented domain. |
Generative AI workloads
20-25% | Implement AI solutions by using Microsoft Foundry
55-60% | AI-901 moves beyond description toward basic implementation using Foundry, Python syntax, Azure resources, APIs, SDKs, and command-line tools. |
Migration checklist
- Stop scheduling, selling, or presenting AI-900 as a current exam.
- Compare completed AI-900 preparation with the current AI-901 guide; do not carry over obsolete weighting or mechanics.
- Create a fresh AI-901 plan around its current domains and hands-on role expectations.
- Remove or quarantine practice content that cannot be mapped to a current objective with source evidence.
- Recheck Microsoft's dated AI-901 study guide before booking because role-based exams change over time.
Open the official AI-901 study guide
Historical Domain Guide
Describe Artificial Intelligence workloads and considerations: Historical Scope
Historically, Describe Artificial Intelligence workloads and considerations required candidates to connect configuration choices with operational outcomes. The official outline included Identify features of common AI workloads; Identify guiding principles for responsible AI. Preserve hands-on understanding of those tasks where the successor comparison shows overlap, but do not treat this retired weighting or terminology as a current exam blueprint.
- Use the official historical objectives to document what describe artificial intelligence workloads and considerations required
- Mark each prior skill as transferable, changed, or absent in the current replacement guide
- Rebuild practical exercises around current services, interfaces, governance, and role expectations
Describe fundamental principles of machine learning on Azure: Historical Scope
Historically, Describe fundamental principles of machine learning on Azure required candidates to connect configuration choices with operational outcomes. The official outline included Identify common machine learning techniques; Describe core machine learning concepts. Preserve hands-on understanding of those tasks where the successor comparison shows overlap, but do not treat this retired weighting or terminology as a current exam blueprint.
- Use the official historical objectives to document what describe fundamental principles of machine learning on azure required
- Mark each prior skill as transferable, changed, or absent in the current replacement guide
- Rebuild practical exercises around current services, interfaces, governance, and role expectations
Describe features of computer vision workloads on Azure: Historical Scope
Historically, Describe features of computer vision workloads on Azure required candidates to connect configuration choices with operational outcomes. The official outline included Identify common types of computer vision solution; Identify Azure tools and services for computer vision tasks. Preserve hands-on understanding of those tasks where the successor comparison shows overlap, but do not treat this retired weighting or terminology as a current exam blueprint.
- Use the official historical objectives to document what describe features of computer vision workloads on azure required
- Mark each prior skill as transferable, changed, or absent in the current replacement guide
- Rebuild practical exercises around current services, interfaces, governance, and role expectations
Describe features of Natural Language Processing (NLP) workloads on Azure: Historical Scope
Historically, Describe features of Natural Language Processing (NLP) workloads on Azure required candidates to connect configuration choices with operational outcomes. The official outline included Identify features of common NLP Workload Scenarios; Identify Azure tools and services for NLP workloads. Preserve hands-on understanding of those tasks where the successor comparison shows overlap, but do not treat this retired weighting or terminology as a current exam blueprint.
- Use the official historical objectives to document what describe features of natural language processing (nlp) workloads on azure required
- Mark each prior skill as transferable, changed, or absent in the current replacement guide
- Rebuild practical exercises around current services, interfaces, governance, and role expectations
Describe features of generative AI workloads on Azure: Historical Scope
Historically, Describe features of generative AI workloads on Azure required candidates to connect configuration choices with operational outcomes. The official outline included Identify features of generative AI solutions; Describe features and capabilities of Azure AI Foundry. Preserve hands-on understanding of those tasks where the successor comparison shows overlap, but do not treat this retired weighting or terminology as a current exam blueprint.
- Use the official historical objectives to document what describe features of generative ai workloads on azure required
- Mark each prior skill as transferable, changed, or absent in the current replacement guide
- Rebuild practical exercises around current services, interfaces, governance, and role expectations
Sources and Verification
Verified 2026-08-26
How this page was made
Cert Atlas compared Microsoft's final AI-900 study guide with the current AI-901 guide at the domain and objective level, then checked Microsoft's retirement list and transition announcement. OpenAI Codex assisted with extraction, normalization, comparison, and drafting. Claims were reviewed against the linked official sources; no exam stems, answers, choices, or explanations were used.
Historical AI-900 Domains
1.0 Describe Artificial Intelligence workloads and considerations
15-20%
- 1.1Identify features of common AI workloads
Identify computer vision workloads; Identify natural language processing workloads; Identify document processing workloads; Identify features of generative AI workloads
- 1.2Identify guiding principles for responsible AI
Describe considerations for fairness in an AI solution; Describe considerations for reliability and safety in an AI solution; Describe considerations for privacy and security in an AI solution; Describe considerations for inclusiveness in an AI solution; Describe considerations for transparency in an AI solution; Describe considerations for accountability in an AI solution
2.0 Describe fundamental principles of machine learning on Azure
15-20%
- 2.1Identify common machine learning techniques
Identify regression machine learning scenarios; Identify classification machine learning scenarios; Identify clustering machine learning scenarios; Identify features of deep learning techniques; Identify features of the Transformer architecture
- 2.2Describe core machine learning concepts
Identify features and labels in a dataset for machine learning; Describe how training and validation datasets are used in machine learning; Describe Azure Machine Learning capabilities; Describe capabilities of automated machine learning; Describe data and compute services for data science and machine learning; Describe model management and deployment capabilities in Azure Machine Learning
3.0 Describe features of computer vision workloads on Azure
15-20%
- 3.1Identify common types of computer vision solution
Identify features of image classification solutions; Identify features of object detection solutions; Identify features of optical character recognition solutions; Identify features of facial detection and facial analysis solutions
- 3.2Identify Azure tools and services for computer vision tasks
Describe capabilities of the Azure AI Vision service; Describe capabilities of the Azure AI Face detection service
4.0 Describe features of Natural Language Processing (NLP) workloads on Azure
15-20%
- 4.1Identify features of common NLP Workload Scenarios
Identify features and uses for key phrase extraction; Identify features and uses for entity recognition; Identify features and uses for sentiment analysis; Identify features and uses for language modeling; Identify features and uses for speech recognition and synthesis; Identify features and uses for translation
- 4.2Identify Azure tools and services for NLP workloads
Describe capabilities of the Azure AI Language service; Describe capabilities of the Azure AI Speech service
5.0 Describe features of generative AI workloads on Azure
20-25%