“We needed a partner who understood both the technical depth of Azure OpenAI and the governance requirements of an enterprise.”
Enterprise Program Brief
Azure AI Fundamentals (AI-900)
Warning This certification has been retired. The current exam is AI-901. Certification-focused foundational program introducing common AI concepts and Azure AI services for teams beginning their AI journey.
Track: Azure AI FundamentalsMicrosoftBeginner
Download Program Brief (PDF)Microsoft Credential Retired
This Microsoft certification can no longer be earned. The current exam is AI-901.
Program Snapshot
Engagement scoped to cohort
Duration1 day
FormatVirtual, On-site, or Hybrid
LevelBeginner
Course CodeAI-900
Certification TrackAzure AI Fundamentals
SubjectAI Fundamentals
- MCT-led delivery
- Private cohort / virtual ILT
- Typical response: 1 business day
Scoping call typically scheduled within one business day
VNode ITeS delivers Azure AI Fundamentals (AI-900) as an MCT-led Microsoft program for Business User, Technical Beginner. Typical duration is 1 day (Virtual, On-site, or Hybrid). Labs follow production-shaped scenarios rather than slide-only walkthroughs. Private cohorts can shift emphasis by role mix, workspace or repo constraints, and rollout timing.
Audience Profile
Built for these roles
Built for business and technical learners who need a structured introduction to AI concepts, responsible AI, and Azure AI capabilities.
- Business User, Technical Beginner
- AI Fundamentals
- Tailored Team Delivery
- Implementation-Focused
Overview
Executive overview
Warning This certification has been retired. The current exam is AI-901. Certification-focused enterprise program aligned to Azure AI Fundamentals (AI-900).
Prerequisites
- Hands-on experience in the relevant Microsoft workload area.
- Familiarity with core product concepts and enterprise delivery expectations.
Program Outcomes
Capabilities your teams will gain
- Strengthen readiness for AI-900 certification objectives
- Build deeper delivery capability in ai fundamentals scenarios
- Prepare teams for more confident project execution in the target Microsoft workload
- Support structured certification-led upskilling across the organization
Enterprise Delivery
Private cohort delivery
VNode-designed, vendor-aligned instructor-led program. Scope role mix, delivery format, and rollout goals for your cohort.
Curriculum
Curriculum roadmap
- AI workloads, use cases, and responsible AI principles
- Machine learning concepts and Azure Machine Learning
- Computer vision and natural language processing workloads
- Generative AI capabilities and Azure OpenAI Service
- Azure AI Services overview and practical applications
1Module 1
AI Workloads and Responsible AI
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Module 1
AI Workloads and Responsible AI
Understand common AI workload types, identify use cases for prediction, anomaly detection, vision, and language. Apply Microsoft responsible AI principles including fairness, reliability, and transparency.
- Understand common AI workload types, identify use cases for prediction, anomaly detection, vision, and language
- Apply Microsoft responsible AI principles including fairness, reliability, and transparency
2Module 2
Machine Learning Fundamentals
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Module 2
Machine Learning Fundamentals
Describe supervised and unsupervised learning concepts, regression, classification, and clustering scenarios, and how Azure Machine Learning supports the ML model lifecycle.
- Describe supervised
- unsupervised learning concepts
- classification
- and clustering scenarios
- and how Azure Machine Learning supports the ML model lifecycle
3Module 3
Computer Vision and NLP Workloads
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Module 3
Computer Vision and NLP Workloads
Understand computer vision scenarios (image classification, object detection, OCR) and NLP workloads (text analytics, language detection, question answering, translation) on Azure AI Services.
- Understand computer vision scenarios (image classification
- object detection
- NLP workloads (text analytics
- language detection
- question answering
4Module 4
Generative AI and Azure OpenAI
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Module 4
Generative AI and Azure OpenAI
Describe generative AI workloads, large language model (LLM) concepts, copilot capabilities, and Azure OpenAI Service features including chat completions, embeddings, and DALL-E.
- Describe generative AI workloads
- large language model (LLM) concepts
- copilot capabilities
- and Azure OpenAI Service features including chat completions
5Module 5
Azure AI Services in Practice
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Module 5
Azure AI Services in Practice
Explore Azure AI Services portfolio including Speech, Language, Vision, and Document Intelligence, understand provisioning models, and identify appropriate services for common scenarios.
- Explore Azure AI Services portfolio including Speech
- and Document Intelligence
- understand provisioning models
- and identify appropriate services for common scenarios
Delivery Models
Delivery models
Virtual ILTOnsiteHybridExecutive WorkshopBootcampWeekend
Engagement Fit
Engagement fit
- Implementation-focused labs
- Private cohort delivery
- Beginner practitioner depth
- Business outcome alignment
Enterprise Customization
Enterprise customization
Tailor this program to your organization's priorities: Creates a common baseline for enterprise AI learning before teams move into implementation-focused programs.
- Align labs to your production environment and platform priorities
- Add focused exam-readiness reviews and instructor-led practice sessions
- Extend into project-specific architecture or delivery coaching
Credentials
Certification & official source
- •Azure AI Fundamentals
Aligned to the official source referenced for this program.
View Official SourceResources
Program resources
Yes. Most enterprise clients prefer private delivery scoped to role mix, timezone, and rollout timeline. We align lab environments and scenarios to your tenant context where applicable.
Enterprise Proof
Trusted delivery outcomes
Retail & E-commerce
Representative Retail Analytics Team
Instead of treating reporting as a tooling issue alone, the work focused on consistency, governance, and shared delivery practices across analysts and engineering teams.
- Higher consistency in report design practices
- Improved collaboration between analysts and engineering teams
Healthcare
Representative Healthcare Product Team
The engagement helped product and engineering stakeholders move from interest in AI to clearer implementation choices, security expectations, and prototyping discipline.
- Stronger alignment between product and engineering teams
- Improved clarity on prototype-to-production requirements
Delivery Capability
Enterprise-grade instruction
MCT-led delivery
Programs led by Microsoft Certified Trainer practitioners
Enterprise program oversight
Founder-led specialist delivery with structured rollout planning
Global delivery
APAC · EMEA · Americas · Virtual & Onsite
Implementation-focused
Hands-on labs aligned to production scenarios
