The short version
This is a review sheet, not a course. Use it in the last week to spot gaps, not to learn a service from zero. It follows the four AI-200 domains from Microsoft’s study guide (“Skills measured as of October 7, 2026”), and it does not claim to cover everything on the exam: Microsoft says related topics may be assessed. Details such as limits, defaults, and names change, so confirm anything critical in the Microsoft Learn documentation.
Exam facts at a glance
| Item | Fact |
|---|---|
| Exam | AI-200, Developing AI Cloud Solutions on Azure |
| Credential | Microsoft Certified: Azure AI Cloud Developer Associate |
| Time | 120 minutes; proctored; may include interactive components |
| Passing score | 700 on a scale of 1 to 1,000 |
| Questions | Number not published for this exam |
| Weightings | Containers 20-25%, data management with vector search 30-35%, connect to services 20-25%, secure/monitor/troubleshoot 20-25% |
| Retakes | 24 hours after the first failed attempt, then 14 days between later ones, five attempts in 12 months |
| Renewal | Annual, free, open-book assessment, window opens six months before expiry |
The study guide’s list of domain headings and its “skills at a glance” differ on data management (25-30% versus 30-35%). Check the live page. Details are on the exam format page.
Domain 1: Containerized solutions (20-25%)
Key concepts
- Azure Container Registry stores images. A Registry Task builds and runs images in Azure so you do not need Docker on your own machine.
- App Service can run a container and supply environment variables and secrets to it.
- Container Apps run containers with revisions. A new revision is a new version of the app; traffic can be split between revisions.
- KEDA scales on event sources such as queue length, and can scale in and out based on them.
- AKS is full Kubernetes: you describe resources in manifest files and read logs and events to debug.
Easily confused pairs
- App Service container: one web app, simple. Container Apps: revisions and event scaling without managing Kubernetes. AKS: you need Kubernetes itself.
- Tag: a movable label. Digest: an exact immutable reference.
- Pod logs: what the app printed. Events: what the cluster did to the pod (pull failures, scheduling).
Decision rules
- “Scale on queue depth, scale to zero, no cluster management” points to Container Apps with KEDA.
- “Build the image in the cloud from source” points to a Registry Task.
- “The pod will not start” means check events first, then logs.
Traps: treating a connectivity problem as an app bug, and picking AKS when the scenario never asks for Kubernetes features.
Domain 2: Data management and vector search (30-35%)
Key concepts
- An embedding is a list of numbers that represents meaning. Similarity search finds the stored vectors nearest to the query vector.
- Cosmos DB for NoSQL: indexing policy, consistency levels, request units (RUs), change feed, and vector indexes. The vector index types are flat, quantizedFlat, and diskANN. Microsoft’s vector search page describes quantizedFlat as a fit up to about 50,000 vectors and diskANN as the most performant above that.
- PostgreSQL: the pgvector extension stores vectors; indexes trade recall, speed, and memory; metadata filters combine with similarity search for retrieval-augmented generation (RAG).
- Azure Managed Redis: caching with expiration and invalidation, plus vector indexing. Search modules need specific settings at creation (Enterprise clustering and a NoEviction policy, per Microsoft’s module page).
Easily confused pairs
- Exact (flat) search: complete results, slower at scale. Approximate index: faster, may miss a near neighbor.
- Strong consistency: freshest, costlier. Eventual: cheapest, may read stale data. Session sits between and fits a single user’s view.
- Expiration (time-based removal) versus invalidation (removal because data changed).
- Change feed: reads changes as they happen. A query: reads current state.
Decision rules
- “Need only the most relevant items for this tenant” means vector search plus a metadata filter.
- “Lots of reads of the same answer” suggests caching; “answer changes often” raises invalidation questions.
- “Lower RU cost for queries” suggests reviewing the indexing policy.
Traps: assuming an index name from one service exists in another, and forgetting that RAG needs retrieval before generation.
Domain 3: Connect to and consume services (20-25%)
Key concepts
- Service Bus: queues, topics, subscriptions, dead-letter queue. A message that exceeds the maximum delivery count (default 10) moves to the dead-letter queue.
- Event Grid: events with filters, custom events, and retry behavior.
- Functions: triggers start a function; bindings connect input and output without custom code.
Easily confused pairs
- Message: work someone must do, so use a queue. Event: a fact that something happened, so use Event Grid.
- Queue: one consumer group takes each message. Topic with subscriptions: many receivers each get a copy.
- Trigger (what starts it) versus binding (what it reads or writes).
Decision rules
- “Must not lose work, process in order, or handle failures” points to Service Bus.
- “Notify many subscribers, filter by type” points to Event Grid.
- “Failed messages pile up” means read the dead-letter queue and find the cause.
Traps: mixing hosting plan, settings, and deployment into one decision.
Domain 4: Secure, monitor, troubleshoot (20-25%)
Key concepts
- Key Vault holds secrets; rotation replaces them over time. App Configuration holds non-secret settings.
- A managed identity lets an app authenticate to Azure services with no stored password.
- OpenTelemetry traces follow a request across services. KQL queries logs and metrics.
Easily confused pairs
- Secret (Key Vault) versus setting (App Configuration).
- Reader-type role (get a secret) versus manager-type role (change it).
- Trace (one request across services) versus log (one event) versus metric (a number over time).
KQL reminders: `where` filters, `summarize` aggregates, `bin()` groups time into buckets, `dcount()` counts distinct values, and the join kind decides what happens to rows with no match.
Decision rules
- “No credentials in code” means managed identity plus Key Vault.
- “Where is the delay?” means traces first, then logs.
Last-day checklist
- Re-read the four weightings and spend the last hours on the largest, data management.
- Write from memory: one vector index per service and when you would pick it.
- Say aloud the message-versus-event rule.
- Recall one managed identity flow from app to secret.
- Run ten small KQL queries.
- Read the exam sandbox once so the interface is familiar.
- Sleep. Rested candidates read constraints more carefully.
More guides for AI-200
- AI-200 Study Plan: 4-Week and 8-Week
- Is the AI-200 Exam Hard?
- AI-200 Prerequisites: What to Know First
- AI-200 Renewal and Retakes Explained
- Is AI-200 Worth It? An Honest Look
- AI-200 vs AI-300: Which Exam First?
- AI-103 vs AI-200: Which Exam Should You Take?
Next step
Test what stuck with the sample questions, and read the domains explained page for the reasoning behind each area. The AI-200 overview links the rest. For timed practice, our AI-200 practice tests are original practice sets.
Frequently asked questions
Is this enough to pass?
No sheet is. It is a review aid. Hands-on practice and Microsoft’s learning material come first.
Can I take notes into the exam?
Check Microsoft’s exam rules and your testing provider’s policy on the day. Do not assume notes are allowed.
Are the vector index names here tested exactly?
They appear in Microsoft’s documentation and are fair to know. The exam may test the idea behind them rather than the name.