Azure AI-103 cheat sheet for key facts, traps, service mappings, and final review.
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Use this cheat sheet for Microsoft Certified: Azure AI Apps and Agents Developer Associate (AI-103) when you need to decide quickly between app architecture, model workflow, agent behavior, Azure AI service fit, and operational controls. This page intentionally avoids exact live exam facts; verify current Microsoft Learn status before final scheduling.
Read every scenario in this order
Name the workload: app/agent planning, generative AI, retrieval, vision, speech, language, extraction, evaluation, or operations.
Identify the Azure boundary: identity, network, data store, model deployment, service endpoint, or tool permission.
Decide whether the answer is about building, securing, evaluating, or monitoring.
Prefer the simplest managed path that satisfies the stated constraint.
Reject answers that improve a demo but skip production proof: auth, telemetry, evaluation, and data governance.
AI-103 answer sequence
Use this when the stem mixes app choice, model fit, retrieval, tools, governance, and monitoring.
flowchart TD
S["Scenario"] --> W["What is the workload trying to do?"]
W --> F["Pick app, RAG, agent, extraction, or safety path"]
F --> G["Check identity, data boundary, and service fit"]
G --> V["Validate with telemetry, evaluation, and rollback"]
Foundry and app planning chooser
Requirement
Strong first answer
choose or deploy a model
Match model capability, latency, cost, region, content policy, and deployment constraints.
build a chat app over enterprise data
Use RAG with governed retrieval, source filtering, prompt grounding, and evaluation.
build an agentic workflow
Define instructions, tools, permissions, state, error handling, and traceability.
connect from code securely
Use managed identity or approved credential flow, not secrets embedded in code.
control production rollout
Use versioned prompts/config, tests, monitoring, approval, and rollback.
prove quality
Use evaluation data for groundedness, relevance, safety, latency, and regression behavior.
Agent decision rules
If the agent must…
Check this before choosing an answer
call a business system
tool contract, input validation, authorization, audit log, and error behavior
retrieve documents
source permissions, index freshness, chunking, ranking, metadata, and citation needs
take an action
human approval, idempotency, rollback, and least-privilege access
process user files
file type, extraction service fit, malware or content checks, and storage boundary
handle sensitive data
redaction, retention, encryption, private access, and logging restrictions
Azure AI service fit
Workload clue
Better starting point
classify, summarize, translate, or understand text
language or generative AI service depending on output flexibility and determinism
extract fields from forms or documents
document extraction/content understanding path with schema validation
analyze images or video frames
vision or multimodal model path, depending on task and output shape
speech-to-text or text-to-speech
speech service path with language, voice, latency, and accessibility constraints
moderate unsafe content
content safety and policy controls, plus workflow ownership
search enterprise content semantically
search/vector index plus retrieval filters and source governance
RAG quality checklist
Quality problem
Stronger fix
answer is plausible but unsupported
improve grounding, retrieval ranking, citation handling, and evaluation
answer uses old information
refresh source ingestion and index update process
answer sees forbidden content
enforce identity-aware filtering and source permissions
answer misses key facts
improve chunking, metadata, query rewriting, and prompt instructions
response is too slow
reduce context, optimize retrieval, choose a suitable model, and measure each hop
behavior regresses after a change
run repeatable evaluations before promotion
Python implementation reminders
Code-path issue
Exam instinct
credentials
Prefer managed identity or secure secret handling. Never hard-code keys in app code.
retries
Retry transient failures with backoff only when the operation is safe to retry.
tool calls
Validate inputs and outputs; log failures without leaking secrets.
telemetry
Emit enough logs, traces, metrics, and correlation IDs to diagnose the request path.
config
Keep endpoints, model deployments, prompts, and thresholds versionable and reviewable.
evaluation
Treat evaluation as part of CI or release gating, not only as a manual spot check.
Security, privacy, and responsible AI
Concern
What the exam usually rewards
least privilege
managed identity, scoped roles, private endpoints where required, and key access review
sensitive input
minimize retention, redact where appropriate, avoid logging prompt secrets, and classify data
unsafe output
content filtering, policy, evaluation, human review, and escalation path
AI-103 answers should look like production Azure AI engineering: correct service fit, scoped identity, governed data, repeatable evaluation, safe agent tools, and observable runtime behavior.