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Azure AI-900 Cheat Sheet

April 13, 2026

Azure AI-900 cheat sheet for key facts, traps, service mappings, and final review.

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Use this for last-mile review. AI-900 is mostly a classification exam: what kind of problem is this, what concept or service family fits it, and which tempting distractor actually solves a different problem? Keep the current Microsoft naming shift in mind: the certification page now says Azure AI Foundry is becoming Microsoft Foundry, but the live AI-900 study guide still uses the older wording.

Grounding: Supplying relevant source context so a generative model can answer from trusted material instead of only from its pretrained knowledge.

Inference: Using a trained model or prebuilt AI service to produce output for new input.

Validation data: Data used to check whether a model generalizes beyond the examples it learned during training.

AI-900 answer sequence

Use this when the stem mixes workload classification, responsible AI, ML basics, or service family choice.

    flowchart TD
	  S["Scenario"] --> W["Classify the workload"]
	  W --> R["Check responsible AI or ML fit"]
	  R --> F["Pick the correct Azure service family"]
	  F --> E["Reject answers that solve a different problem"]

Fast lane picker

If the question is really about… Focus first on… Strongest first move
what kind of problem is being solved workload classification decide vision, language, speech, document, ML, or generative AI before naming a service
ethics, bias, trust, or human oversight responsible AI map the scenario to fairness, safety, privacy, inclusiveness, transparency, or accountability
labeled vs unlabeled data ML basics decide classification, regression, or clustering first
image, document, OCR, or face clue vision lane identify the output type before the Azure service
speech, translation, sentiment, or entities NLP lane separate text-analysis tasks from audio tasks
chat, summarization, copilots, or grounded answers generative-AI lane decide whether the task truly creates content or only extracts or classifies it

Workload chooser

Requirement Strongest first fit Why
classify or detect things in images computer vision the output is about visual content
extract text or structure from forms, invoices, or receipts document processing the clue is about reading an existing document, not generating content
detect sentiment, entities, or key phrases in text NLP the system is analyzing human language
convert audio into text or text into speech speech the input or output starts with audio
predict a label or number from historical data machine learning the task is predictive rather than prebuilt perception
draft, summarize, rewrite, or chat generative AI the system creates new content

Responsible-AI quick rules

Principle Fast anchor Look for clues like…
fairness avoid unjust bias in outcomes different groups receiving systematically weaker decisions
reliability and safety behave dependably and reduce harm dangerous failure modes, unsafe outputs, weak fallback paths
privacy and security protect data and access sensitive prompts, exposed data, weak controls, unsafe sharing
inclusiveness work for varied users and conditions accent bias, disability barriers, language coverage gaps
transparency make AI use and limits understandable users do not know AI is involved or cannot interpret the result
accountability humans remain responsible no owner, no escalation path, no review step for risky output

Machine-learning quick rules

Concept Use it when… Do not confuse it with…
classification the output is a category such as approve or deny regression
regression the output is a number such as sales or delay classification
clustering you want unlabeled grouping classification
supervised learning labels already exist unsupervised learning
deep learning the pattern is complex, often in images, audio, or text all ML being generative by default
transformer architecture modern language and generative models rely on it a separate exam lane about low-level model internals
training the model learns from historical examples inference
inference the trained model produces output on new input training

Computer-vision quick rules

Scenario clue Strongest first answer
assign the whole image to a category image classification
identify what objects are present and where they appear object detection
read printed or handwritten text from an image OCR
locate a face or analyze face-related attributes facial detection or facial analysis
read fields and layout from a form or receipt document processing first, not generic image tagging

NLP and speech quick rules

Scenario clue Strongest first answer
detect sentiment, entities, key phrases, or summary Azure AI Language capability
convert spoken words into text Azure AI Speech capability
create spoken audio from text Azure AI Speech capability
translate text between languages translation capability
use a model to predict the next words in a sequence language modeling

Generative-AI quick rules

Requirement Strongest first fit Why
draft, summarize, rewrite, or answer conversationally generative AI new content is being created
answer from trusted internal content grounded generation retrieval context reduces unsupported answers
extract exact fields from an invoice document processing the task is reading existing structure, not generating it
predict churn, fraud, or pass/fail classification model the output is a label, not generated language
compare candidate models at a high level model catalog or evaluation capability AI-900 stays conceptual, not implementation heavy

Azure service-fit quick rules

Requirement Strongest first fit
custom training, experimentation, deployment, lifecycle management Azure Machine Learning
image analysis and OCR Azure AI Vision
face-related detection and analysis Azure AI Face detection service
text analytics and language understanding Azure AI Language
speech recognition, synthesis, or audio-first translation Azure AI Speech
large language model access on Azure Azure OpenAI Service
high-level generative-AI workspace, model access, and orchestration surface Azure AI Foundry / Microsoft Foundry naming transition
compare or browse available model choices Azure AI Foundry model catalog

Commonly confused pairs

Pair Keep this distinction clear
document processing vs generative AI extract existing information vs create new content
document processing vs generic computer vision form and layout reading vs general scene analysis
classification vs regression category vs number
classification vs clustering labeled prediction vs unlabeled grouping
training vs inference learning stage vs usage stage
Azure Machine Learning vs prebuilt Azure AI service custom model lifecycle vs ready-made AI capability
Azure AI Language vs Azure AI Speech text-first workload vs audio-first workload
grounding vs fine-tuning runtime context vs changing model behavior through training
Azure AI Foundry vs Azure OpenAI Service broader generative-AI platform surface vs model service access

Last 15-minute review

Review this Because it fixes…
workload classification order early service-choice mistakes
responsible-AI principles abstract-but-testable concept misses
regression vs classification vs clustering core ML confusion
training, validation, overfitting, and inference ML vocabulary drift
Vision vs Face vs Language vs Speech vs Azure Machine Learning service-family mispicks
grounding vs generic generation generative-AI distractors

Keep going

Revised on Sunday, May 10, 2026
Study Plan
Sample Questions