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Microsoft AI-901 Cheat Sheet: AI Fundamentals

May 1, 2026

Review Microsoft Azure AI Fundamentals (AI-901) concepts, Microsoft Foundry implementation awareness, responsible AI, prompts, models, and Azure AI service traps before practicing in IT Mastery.

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AI-901 is the newer Azure AI Fundamentals route. Use this cheat sheet to separate foundational AI concepts from Microsoft Foundry implementation awareness before you start the free diagnostic or timed practice.

Use this with practice. Review the AI-901 distinctions, then take the free diagnostic or open the full AI-901 route in IT Mastery.

Try AI-901 on Web Free AI-901 diagnostic

Exam snapshot

FieldDetail
IssuerMicrosoft
Certification nameMicrosoft Certified: Azure AI Fundamentals
Exam codeAI-901
Version label in IT Mastery catalogBeta
Practice reference50 questions in 45 minutes
IT Mastery statusLive AI-901 practice available

Domain map

DomainPractice weightWhat to knowCommon trap
Identify AI concepts and capabilities43%AI workloads, responsible AI, data quality, model behavior, generative AI concepts, and service categoriesMemorizing service names before recognizing the workload
Implement AI solutions by using Microsoft Foundry57%Foundry projects, model deployment, prompts, lightweight clients, text, speech, vision, image generation, and Content UnderstandingTreating Foundry as only a chat interface instead of an AI app-building environment

Must-know distinctions

DistinctionHow to decide
AI concept vs Azure implementationFirst identify the AI workload or risk; then choose the Azure or Foundry feature that supports it.
Classification vs regressionClassification predicts categories; regression predicts numeric values.
Generative AI vs extractionGenerative AI creates new content; extraction identifies fields or facts from existing content.
Prompt vs groundingA prompt gives instructions; grounding connects the response to source content.
Model deployment vs application clientDeployment exposes a model; the client sends prompts, inputs, and parameters to use it.
Text vs speech vs visionMatch the service family to the input and output type before choosing a feature.
Responsible AI fairness vs reliabilityFairness checks unequal impact; reliability checks safe and consistent behavior.
Content safety vs evaluationContent safety moderates harmful content; evaluation measures quality, grounding, and risk.

High-yield checklist

  • Identify the workload: prediction, classification, generation, transcription, translation, vision, or extraction.
  • Check whether the question asks for a concept, a Foundry action, or an Azure AI service family.
  • Keep responsible AI principles attached to concrete risks: fairness, reliability, privacy, transparency, safety, and accountability.
  • Use Foundry implementation awareness for projects, deployments, prompts, model access, and evaluation.
  • Use grounding when answers must be based on private, current, or cited source content.
  • Match speech workloads to audio inputs and text workloads to written language inputs.
  • Distinguish image analysis from document extraction and Content Understanding.
  • Treat generated answers as output that may require validation, safety controls, and user guidance.
  • Watch for Python or lightweight client cues when a scenario asks how an app uses a model.
  • Verify current Microsoft exam guidance before exam day while AI-901 remains a newer route.

Common traps

  • Choosing a generative AI answer for a simple classification or extraction problem.
  • Ignoring data quality when the scenario is about biased or unreliable output.
  • Treating prompts as a substitute for grounding, evaluation, or content-safety controls.
  • Confusing model deployment with training a custom model.
  • Choosing computer vision when the requirement is speech, text, or document structure.
  • Missing responsible AI concerns because the question uses simple fundamentals-level wording.

Practice strategy

Start with the free AI-901 diagnostic and tag misses as concept recognition or Foundry implementation. If concept misses dominate, review workload categories and responsible AI. If Foundry misses dominate, drill prompts, deployments, clients, model use, grounding, and AI service families.

AI-901 is a fundamentals route, so the goal is not deep engineering memorization. The goal is accurate recognition of the workload, service family, risk boundary, and basic implementation step.

  • AI-900 if you are comparing the retiring Azure AI Fundamentals route
  • AI-103 if your next step is Azure AI apps and agents
  • AI-200 if your next step is AI cloud development
Revised on Monday, May 25, 2026
Implement AI Solutions by Using Microsoft Foundry
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