Plan + Manage Solution
20-25%of exam
Generative AI
15-20%of exam
Agentic Solution
5-10%of exam
Computer Vision
10-15%of exam
Natural Language Processing
15-20%of exam
Knowledge Mining
15-20%of exam
Quick Facts
- Exam
- AI-102
- Credential
- Azure AI Engineer
- Time
- 100 min
- Pass
- 700/1000
- Questions
- 40-60
- Fee
- $165 USD
- Blueprint
- Dec 23 2025
- Retired
- June 30 2026
Six Skill Areas
Plan | GenAI | Agent | Vision | NLP | Mining
Plan: 20-25%GenAI: 15-20%Agent: 5-10%Vision: 10-15%NLP: 15-20%Mining: 15-20%
Multi vs Single Service
Multi-service
- One endpoint/key
- Shared billing
- Fast start
Single-service
- Dedicated endpoint
- Per-service RBAC
- Network isolation
Simple vs isolated
Service Picker
- Generate text/images→Azure OpenAI(Generative)
- Analyze images→Azure AI Vision(Prebuilt)
- Own image classes→Custom Vision(Trained)
- Extract form fields→Document Intelligence(Structured)
- Search your content→Azure AI Search(Retrieval)
- Transcribe audio→Azure AI Speech(STT)
- Detect user intent→CLU(Language)
- Multimodal extraction→Content Understanding(Foundry Tools)
Resources + Endpoints
- Multi-service resource
- One key many services
- Single-service resource
- Dedicated endpoint per service
- Azure OpenAI resource
- Own kind OpenAI
- Endpoint
- Service base URL
- Key pair
- Two rotatable keys
- Region
- Fixed by resource
- kind
- Resource type flag
Responsible AI Layers
Filters | Blocklists | Shields | Harm detection
Filters: severity levelsBlocklists: exact termsShields: jailbreak blockHarm: category scores
Key vs Managed Identity
API key
- Shared secret
- Manual rotation
- Leak risk
Managed identity
- No secrets
- Entra tokens
- RBAC scoped
Secret vs identity
Auth Picker
- Azure-hosted app→Managed identity(No secrets)
- Local dev + prod→DefaultAzureCredential(Chained)
- Grant resource access→Azure RBAC(Roles)
- Private clients only→Private endpoint(Disable public)
- Getting 429→Backoff retry(Check quota)
Auth + Security
- Managed identity
- No stored secrets
- DefaultAzureCredential
- Chained auth methods
- Microsoft Entra
- Token-based identity
- Azure RBAC
- Role-based authorization
- Private endpoint
- Private network only
- Disable public access
- Close public endpoint
- 429 throttle
- Retry exponential backoff
Content Safety vs Filters
Content Safety
- Standalone service
- Any content
- Text and images
OpenAI filters
- Built into deployment
- Prompt/completion only
- Not external content
Anywhere vs model-only
Deploy + Ops
- IaC
- Repeatable resource templates
- CI/CD
- Automated deployment pipeline
- Container
- Local/edge deployment
- Azure Monitor
- Metrics and logs
- Diagnostic settings
- Route resource logs
- Cost management
- Track and budget
- Quota
- Rate and token limits
Responsible AI
- Content Safety
- Standalone content moderation
- Content filters
- Model prompt/completion filter
- Blocklists
- Exact banned terms
- Harm categories
- Hate sexual violence self-harm
- Severity thresholds
- Per-category block levels
- Prompt Shields
- Block jailbreak attacks
- Model card
- Intended use limitations
Generative Flow
Deploy | Prompt | RAG | Evaluate | Optimize
Deploy: modelPrompt: templateRAG: groundEvaluate: scoreOptimize: tune
Fine-tune vs RAG
Fine-tune
- Change behavior/style
- Labeled JSONL
- Slower iteration
RAG
- Add fresh grounding
- No retraining
- Cheaper iteration
Behavior vs knowledge
Azure OpenAI + Foundry
- Foundry hub
- Shared compute and connections
- Foundry project
- One solution workspace
- Azure OpenAI
- GPT DALL-E embeddings
- Standard deployment
- Pay per token
- Provisioned throughput
- Reserved predictable capacity
- DALL-E
- Image generation model
- Embedding model
- Text to vectors
Prompt + Tuning
- Prompt flow
- Reusable multi-step pipeline
- Prompt template
- Reusable parameterized prompt
- System message
- Durable role instructions
- Temperature
- Controls randomness
- Fine-tuning
- Train on examples
- Evaluation
- Score models and flows
- Grounding
- Add your data
Agents
- Agent
- Reasoning plus tools
- Tool
- Callable agent capability
- Foundry Agent Service
- Managed agent hosting
- Agent Framework
- Code-first complex agents
- Supervisor pattern
- Routes specialist agents
- Orchestration
- Coordinate multi-agent workflow
- Guardrails
- Max turns limits
Classification vs Detection
Classification
- What is present
- Whole-image label
- No boxes
Object detection
- What and where
- Bounding boxes
- Location matters
What vs where
Vision Services
- Image Analysis
- Prebuilt tags and captions
- OCR / Read
- Extract printed handwritten text
- Custom Vision
- Train your images
- Classification
- What is present
- Object detection
- What and where
- Compact domain
- Exportable edge model
- Video Indexer
- Video insights and transcripts
- Spatial Analysis
- People movement in video
CLU vs Question Answering
CLU
- Intents + entities
- Bounded actions
- Trained utterances
Question answering
- Knowledge base
- FAQ + docs
- Best answer match
Action vs answer
Language (Text)
- Sentiment analysis
- Positive negative neutral mixed
- Key phrase extraction
- Main text points
- NER
- Detect named entities
- PII detection
- Find redact sensitive data
- Language detection
- Identify text language
- Translator
- Text and document translation
Speech + CLU
- Speech-to-text
- Transcribe spoken audio
- Text-to-speech
- Synthesize spoken output
- SSML
- Control pronunciation and prosody
- Custom Speech
- Adapt domain vocabulary
- CLU
- Intents and entities
- Question answering
- Knowledge base answers
- Custom Translation
- Domain-tuned translator model
Search Stack
Index | Indexer | Skillset | Query
Index: storeIndexer: ingestSkillset: enrichQuery: retrieve
Keyword vs Vector Search
Keyword
- Exact terms
- Lexical BM25
- Literal match
Vector
- Embedding similarity
- Semantic meaning
- Handles synonyms
Literal vs meaning
RAG + Search Picker
- Ground model answers→RAG pattern(Retrieve + prompt)
- Exact keyword match→Lexical search(BM25)
- Meaning similarity→Vector search(Embeddings)
- Best default search→Hybrid + semantic(Combined)
- Persist enrichments→Knowledge store(Projections)
- Enrich while indexing→Skillset(OCR + phrases)
AI Search
- Index
- Searchable document store
- Indexer
- Pull and populate
- Skillset
- Enrich during indexing
- Knowledge store
- Persist enriched projections
- Vector field
- Embedding similarity search
- Hybrid search
- Keyword plus vector
- Semantic ranker
- Rerank by meaning
- Vectorizer
- Query text to vector
Doc Intelligence
- Prebuilt model
- Invoice receipt layout ID
- Custom model
- Train your forms
- Composed model
- Route multiple submodels
- Layout model
- Tables structure and text
- Content Understanding
- Multimodal schema extraction
- Knowledge mining
- Search across documents
Common Traps
Resource type
OpenAI needs own ≠ Others share multi-service
Auth method
Keys are secrets ≠ Managed identity tokenless
Moderation scope
Content Safety anywhere ≠ Filters model-only
Vision task
Classification labels image ≠ Detection locates objects
Language task
CLU classifies intent ≠ QnA matches answer
Search type
Keyword matches terms ≠ Vector matches meaning
Grounding vs training
RAG adds context ≠ Fine-tune changes behavior
Last Minute
- 1.Pass = 700 of 1000
- 2.100 minutes, 40-60 questions
- 3.Six weighted skill areas
- 4.OpenAI needs own resource
- 5.Managed identity over keys
- 6.Content Safety moderates anywhere
- 7.RAG grounds, fine-tune shapes
- 8.Classification labels; detection locates
- 9.CLU intents; QnA answers
- 10.Hybrid plus semantic ranker
- 11.Prompt Shields block jailbreaks
- 12.Prebuilt before custom models
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