AI Workloads
15-20%of exam
Machine Learning
15-20%of exam
Computer Vision
15-20%of exam
NLP Workloads
15-20%of exam
LanguageSpeechTranslatorSentimentKey Phrases
Generative AI
20-25%of exam
Azure OpenAIPromptsRAGCopilotResponsible GenAI
Quick Facts
- Exam
- AI-900
- Credential
- Azure AI Fundamentals
- Time
- 45 min
- Pass
- 700/1000
- Level
- Fundamentals
- Retires
- Jun 30 2026
- Prereq
- None
Responsible Six
Fair safe private inclusive transparent accountable
FairnessReliabilityPrivacyInclusivenessTransparencyAccountability
Workload Picker
- Images or video→Vision
- Forms or invoices→Document Intelligence
- Text sentiment→Language
- Speech audio→Speech
- Translate text→Translator
- Generate content→Azure OpenAI
Responsible AI
- Fairness
- Avoid bias
- Reliability
- Work safely
- Privacy
- Protect data
- Inclusiveness
- Serve everyone
- Transparency
- Explain behavior
- Accountability
- Human responsibility
AI Workloads
- Vision
- Images/video
- NLP
- Human language
- Speech
- Audio language
- Document processing
- Forms/text extraction
- Anomaly detection
- Unusual patterns
- Generative AI
- Creates content
- Recommendation
- Suggest items
ML Targets
Number, label, group
RegressionClassificationClustering
Regression vs Classification
Regression
- Numeric target
- Price/quantity
- Continuous output
Classification
- Category target
- Yes/no
- Class label
Number vs label
ML Picker
- Predict price→Regression
- Predict category→Classification
- Group customers→Clustering
- Image recognition→Deep learning
- Train without code→AutoML
- Serve predictions→Endpoint
ML Concepts
- Feature
- Input variable
- Label
- Target value
- Training set
- Learn patterns
- Validation set
- Tune model
- Test set
- Final check
- Model
- Learned function
- Inference
- Use model
Classification vs Clustering
Classification
- Known labels
- Supervised
- Predict class
Clustering
- No labels
- Unsupervised
- Find groups
Labels vs groups
ML Techniques
- Regression
- Predict number
- Classification
- Predict class
- Clustering
- Find groups
- Deep learning
- Neural networks
- Transformer
- Attention architecture
- AutoML
- Automate training
- Metric
- Measure performance
Azure ML
- Workspace
- ML container
- Designer
- Visual pipelines
- Automated ML
- Model search
- Compute
- Training resources
- Dataset
- Data asset
- Endpoint
- Model serving
Vision vs Document
Vision
- Images
- Objects
- Captions
Document
- Forms
- Tables
- Fields
Scene vs form
Vision Picker
- Read image text→OCR
- Describe image→Image Analysis
- Detect objects→Object detection
- Classify custom images→Custom Vision
- Extract form fields→Document Intelligence
- Detect faces→Face
Vision Services
- Image Analysis
- Tags/captions
- OCR
- Read text
- Face
- Detect faces
- Custom Vision
- Custom classifier
- Object detection
- Locate objects
- Spatial analysis
- People movement
- Content safety
- Moderate content
Document Intelligence
- Prebuilt model
- Common documents
- Custom model
- Your forms
- Layout
- Text/tables
- Invoice
- Billing fields
- Receipt
- Purchase fields
- Confidence
- Extraction score
Language Stack
Text, speech, translate
LanguageSpeechTranslator
Speech vs Translator
Speech
- Audio input
- Transcription
- Voice output
Translator
- Text input
- Language conversion
- No audio
Audio vs text
Language Services
- Sentiment
- Opinion score
- Key phrases
- Important terms
- Entity recognition
- Named things
- PII detection
- Sensitive text
- Summarization
- Shorten text
- Question answering
- Knowledge answers
- Conversational language
- Intent/entities
Speech + Translate
- Speech-to-text
- Audio to words
- Text-to-speech
- Words to audio
- Speech translation
- Audio translated
- Translator
- Text translation
- Custom voice
- Brand voice
- Pronunciation
- Speaking assessment
RAG Flow
Retrieve facts before generation
SearchGroundGenerateCite
Prompt vs RAG
Prompt
- Instruction
- Context words
- Model input
RAG
- Retrieve facts
- Ground output
- External data
Ask vs ground
GenAI Picker
- Need factual answers→RAG
- Need search memory→Embeddings
- Need task assistant→Copilot
- Need custom app→Azure OpenAI
- Need better output→Prompt engineering
- Need safety controls→Content filters
Generative AI
- LLM
- Text generation
- Prompt
- Input instruction
- Completion
- Generated output
- Grounding
- Use facts
- RAG
- Retrieve then generate
- Embeddings
- Vector meaning
- Copilot
- AI assistant
- Azure OpenAI
- Hosted models
OpenAI vs Copilot
Azure OpenAI
- Build apps
- Model APIs
- Custom prompts
Copilot
- Ready assistant
- Product embedded
- Task help
Platform vs assistant
Common Traps
Regression vs classification
Regression predicts numbers ≠ Classification predicts labels
Vision vs OCR
Vision sees images ≠ OCR reads text
Translator vs Speech
Translator handles text ≠ Speech handles audio
Prompt vs grounding
Prompt instructs model ≠ Grounding supplies facts
Responsible AI
Fairness reduces bias ≠ Accountability stays human
AI-900 timing
Exam retires June ≠ Finish before retirement
Last Minute
- 1.AI-900 retires Jun 30 2026
- 2.GenAI is 20-25%
- 3.Regression = number
- 4.Classification = label
- 5.Clustering = unlabeled groups
- 6.OCR = read image text
- 7.Language = text analytics
- 8.Speech = audio
- 9.RAG = retrieve then generate
- 10.Responsible AI has six principles
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