Azure AI-901 cheat sheet for key facts, traps, service mappings, and final review.
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Use this cheat sheet for Microsoft Certified: Azure AI Fundamentals (AI-901) when you need fundamentals-level recall without overbuilding the answer. AI-901-style preparation should separate workload recognition, responsible AI, basic implementation awareness, and Microsoft Foundry vocabulary.
AI-901 answer sequence
Use this when the stem mixes workload recognition, responsible AI, data readiness, or service fit.
flowchart TD
S["Scenario"] --> W["Classify the workload"]
W --> R["Check responsible AI or ML fit"]
R --> D["Check data readiness and source governance"]
D --> F["Pick the correct Azure service family"]
Fundamentals triage
Identify the AI workload before naming a service.
Decide whether the question is conceptual, responsible-AI, Azure-resource, or light implementation.
Keep the answer fundamentals-level unless the stem clearly asks for engineering depth.
Prefer service recognition and safe configuration over custom architecture.
Do not treat generative AI as the answer to every AI scenario.
Workload chooser
Scenario clue
Likely workload
predict a category or value from historical examples
machine learning
identify objects, faces, text, or visual features
computer vision
transcribe, synthesize, or translate spoken language
speech
classify sentiment, extract entities, translate text, or summarize language
natural language processing
generate text, code, images, summaries, or conversational responses
generative AI
extract fields from forms, invoices, contracts, or receipts
document intelligence or content extraction
answer questions from company documents
retrieval grounded generative AI
Responsible AI map
Principle
Exam meaning
fairness
reduce harmful bias or unequal treatment
reliability and safety
make behavior dependable, tested, and safe for intended use
privacy and security
protect data, identity, access, and sensitive outputs
inclusiveness
design for diverse users and accessibility needs
transparency
make system behavior, limitations, and AI involvement understandable
accountability
assign human ownership for outcomes, monitoring, and correction
Foundry and generative AI basics
Term
What to remember
model
system that produces predictions, classifications, embeddings, or generated output
prompt
instruction or input supplied to a generative model
system instruction
higher-priority guidance that shapes assistant behavior
grounding
connecting output to trusted external information
embedding
vector representation used for semantic similarity and retrieval
agent
AI workflow that can use instructions and tools to complete tasks
evaluation
repeatable check of quality, safety, groundedness, relevance, or latency
Service-fit instincts
If the question asks for…
Avoid this mistake
a deterministic field extraction
do not default to open-ended chat output
safe AI deployment
do not ignore content safety, privacy, and human oversight
basic Python/API use
do not answer only with portal clicks if the stem mentions code
model output quality
do not assume model size is the only lever
enterprise data answers
do not ignore retrieval permissions and source freshness
fundamentals-level concept
do not choose a complex custom ML pipeline unless required
Machine learning basics
Concept
Fast recall
feature
input variable used by a model
label
target value predicted in supervised learning
training data
examples used to learn model behavior
test data
held-out data used to evaluate generalization
classification
predicts a category
regression
predicts a numeric value
clustering
groups similar records without predefined labels
overfitting
model performs well on training data but poorly on new data
Python and Azure resource awareness
Area
Fundamentals-level expectation
variables and functions
know simple code shape and what value is passed or returned
API or SDK call
recognize endpoint, credential, request, response, and error handling basics
identity
know that credentials and access should be managed securely
resource
understand that Azure services are created, configured, secured, monitored, and billed
keys and secrets
do not hard-code or expose them
monitoring
know that deployed AI behavior needs logs, metrics, and review
Common traps
Trap
Better instinct
Treating AI-901 as only old AI-900 service matching
Expect more implementation, Foundry, and lightweight Python awareness.
Calling every workload generative AI
Separate prediction, classification, extraction, vision, speech, NLP, and generation.
Skipping responsible AI
Tie principles to concrete design choices and user impact.
Choosing advanced engineering too early
Fundamentals questions usually reward clean recognition and safe defaults.
Forgetting data privacy
Prompts, files, training examples, logs, and outputs can all contain sensitive data.
Final 15-minute review
If the stem says…
Start here
fairness, privacy, transparency, accountability
responsible AI principle and matching control
predict, classify, cluster, or train
machine learning concept and data split
image, audio, document, language
specialized AI workload family
prompt, assistant, generated answer
generative AI, grounding, safety, and evaluation
Python, SDK, endpoint, key
basic implementation flow and secure credential handling
company documents or knowledge base
retrieval grounding and source permission awareness
Practice fit
Use IT Mastery for the exact product route, practice status, spaced review when available, and close-answer explanation practice as coverage expands.
AI-901 answers should be simple but precise: identify the workload, apply responsible AI, recognize the Azure or Foundry building block, and avoid turning a fundamentals question into a professional architecture design.