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Azure AI-200 Cheat Sheet

April 24, 2026

Azure AI-200 cheat sheet for key facts, traps, service mappings, and final review.

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Use this cheat sheet for Microsoft AI Cloud Developer route (AI-200) after you know the basics but before you start a timed practice block. The goal is not to memorize a vendor catalog; the goal is to classify the scenario and reject attractive wrong answers quickly.

AI-200 answer sequence

Use this when the stem mixes hosting, events, vector support, or operational safety for AI-enabled apps.

    flowchart TD
	  S["Scenario"] --> H["Classify the hosting or integration lane"]
	  H --> D["Check data and vector support"]
	  D --> O["Check security and observability"]
	  O --> V["Verify rollback, telemetry, or retry behavior"]

First-pass question triage

  1. Name the tested lane before reading the answer choices.
  2. Underline the constraint: security, cost, reliability, latency, governance, implementation effort, or evidence.
  3. Reject answers that solve a neighboring problem but not the stated requirement.
  4. Prefer the smallest correct control, service, workflow, or command that satisfies the constraint.
  5. Look for proof: logs, tests, metrics, policy evidence, deployment status, evaluation results, or user-visible recovery.

What to know cold

Lane Decision rule Reject when
Application hosting Choose between app services, Functions, containers, APIs, and background workers for AI-enabled apps. Picking the newest AI service when the real constraint is hosting, scale, routing, or deployment.
Integration and events Use queues, topics, event routing, API boundaries, retries, and idempotency for reliable app flow. Leaving synchronous request paths overloaded when the scenario asks for durability or loose coupling.
Data and vector support Match Cosmos DB, relational stores, cache, search, and vector patterns to app requirements. Treating vector search as a replacement for data modeling, permissions, or data quality.
Security and observability Apply identity, secrets, logging, tracing, metrics, and safe rollout patterns. Hard-coding secrets or shipping without telemetry because the stem focuses on features.

Common traps and better instincts

Trap Better instinct
Building AI before application plumbing First solve identity, data path, retry behavior, and operational ownership.
Overusing synchronous calls Use queues, events, and background processing when latency or reliability requires it.
Unsecured vector or prompt data Apply the same access, retention, encryption, and audit thinking as any other sensitive data.
No rollback or monitoring plan Prefer answers with deployment slots, health checks, alerts, and traceable failures.

Final 15-minute review

If the stem says Start with
least privilege, private access, compliance, or audit identity scope, data boundary, policy enforcement, logging, and ownership
least operational effort managed service, native integration, simple workflow, and fewer moving parts
high availability, recovery, or outage failure domain, recovery objective, health check, rollback, and validation
performance, scale, or cost bottleneck evidence, traffic pattern, sizing, caching, batching, and quotas
troubleshoot, diagnose, or investigate symptom, recent change, logs, metrics, status, dependency, and smallest safe test

Practice fit

Use IT Mastery for the exact product route, practice status, spaced review when available, and close-answer explanation practice as coverage expands.

Open the exact IT Mastery route here: AI-200 on MasteryExamPrep.

Decision order

For developer lanes, choose the hosting boundary, decouple fragile work, protect identities and data, then prove behavior with telemetry.

Revised on Sunday, May 10, 2026
Study Plan