The short version
Pick the 4-week plan if you already run models or deployments on Azure. Pick the 8-week plan if Azure Machine Learning, Microsoft Foundry, or Bicep are new to you. Both plans put hands-on work first, because AI-300 asks which setting, command, or feature fits a situation, and you remember that best after you have clicked, typed, and broken it once.
How many hours?
Study time varies a lot. As a rough guide, people with day-to-day Azure ML and DevOps experience often need 30 to 50 hours. People who know machine learning but not Azure operations often need 60 to 90. If you are new to both, plan for more and consider the 8-week route with extra lab time. These are general estimates, not Microsoft figures. Adjust after your first practice exam.
Before you start, open Microsoft’s AI-300 study guide and keep it next to you. Tick off each bullet as you can explain it and do it. You need an Azure subscription where you can create a workspace, a Foundry project, and a model deployment. Delete resources when you finish a session, because idle compute and deployments cost money.
The weightings drive the time split: roughly 15-20% MLOps infrastructure, 25-30% model lifecycle, 20-25% GenAIOps infrastructure, 10-15% quality and observability, and 10-15% optimization. Details are on the domains explained page.
The 4-week plan (about 8 to 12 hours a week)
Week 1: infrastructure for both worlds (domains 1 and 3, first half). Create a workspace, a datastore, a compute cluster, and an environment from the CLI and from YAML. Deploy the same workspace with Bicep and run it from a GitHub Actions workflow with OIDC. Lock down access with RBAC and a managed identity. Then create a Foundry resource and project and assign roles.
Week 2: the model lifecycle (domain 2). Track a training job with MLflow, run a sweep, build a two-step pipeline, register a model, and deploy it to a managed online endpoint. Practice a blue/green rollout with a traffic split and with mirrored traffic. Set up a batch endpoint. Finish by configuring drift monitoring and an alert.
Week 3: generative AI operations (domains 3, 4, and 5). Deploy a model with a standard and a provisioned deployment, and read about spillover. Version a prompt in Git and compare two variants. Run an evaluation on a small dataset with quality and safety evaluators. Turn on tracing and look at token and latency metrics. Tune a small RAG app: chunk size, similarity threshold, hybrid search. Read how fine-tuning jobs report training and validation loss.
Week 4: practice and repair. Take a full practice exam early in the week, cold. List every missed topic by domain, fix the weakest domain with a short lab, then take a second exam two days later. Keep the last three days for the checklist below.
The 8-week plan (about 6 to 10 hours a week)
| Week | Focus | What to do |
|---|---|---|
| 1 | Azure and MLOps basics | Review RBAC, managed identities, storage, and networking. Learn the Azure CLI and basic Git. |
| 2 | Workspace resources (domain 1) | Workspaces, datastores, compute, data assets, environments, components, registries. |
| 3 | Infrastructure as code (domain 1) | Bicep, CLI deployment, GitHub Actions, private networking for a workspace. |
| 4 | Training and registration (domain 2) | MLflow, AutoML, sweeps, pipelines, distributed training, model registration. |
| 5 | Deploy and monitor (domain 2) | Online and batch endpoints, rollout and rollback, drift, retraining triggers. |
| 6 | Foundry platform (domain 3) | Resources, projects, identity, private networking, deployment types, PTUs, prompt versioning. |
| 7 | Evaluate, observe, optimize (domains 4 and 5) | Evaluators, safety checks, tracing, cost metrics, RAG tuning, fine-tuning. |
| 8 | Practice and repair | Two or three full practice exams, with a lab session for each weak topic in between. |
Using practice exams in the plan
Take one early as a baseline, even if you expect a low score. It tells you where your time goes. Review every explanation, including the questions you got right, because a lucky guess hides a gap. Do not retake the same set the next day, since you will remember the answers rather than the concept. Our five AI-300 practice tests are built to be used in order, from core concepts to a full-length mix, and they report your score per domain. Aim to score consistently well above your own target before you book, since practice at home is easier than a proctored exam. Our how to study page has more on spacing and review.
The last three days
- Three days out: take one final mixed practice exam under timed conditions. Review misses, but do not start a new topic.
- Two days out: skim your notes on easily confused pairs, such as live traffic split versus mirrored traffic, or identity-based versus credential-based datastores. Redo one small lab you found hard.
- The day before: stop studying by early evening. Check your ID, your booking, and your testing space. The exam day guide has a checklist.
- Exam day: eat, arrive or log in early, and check the clock against the number of questions at the start and again halfway. Mark hard ones and move on.
More guides for AI-300
- AI-300 Exam Format, Scoring and Retakes
- AI-300 Sample Questions With Answers
- AI-300 Prerequisites and Skills Check
- AI-300 Renewal and Retakes Explained
- Is AI-300 Worth It? An Honest Look
- AI-300 Cheat Sheet: Key Facts to Review
- AI-200 vs AI-300: Which Exam First?
Next step
If you are still deciding whether to start, read is AI-300 hard?. If you are ready to measure yourself, start with test 1 of the AI-300 practice tests.
Frequently asked questions
Is reading enough?
Reading helps, but this exam is about operating real resources. Hands-on time makes the scenario questions much easier to read.
Do I need a paid Azure subscription?
You need one where you can create the resources. Costs depend on what you run, so delete resources after each lab.