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Microsoft · AI-300 · Machine Learning Operations Engineer Associate
DP-100 asked whether you could train a model. AI-300 asks whether you can keep it alive.
Microsoft's MLOps exam covers the whole life of a model after it works: training pipelines and experiment tracking, registration and versioning, progressive rollout, drift monitoring and automatic retraining. Then it does the same again for generative AI — Foundry environments, evaluation metrics, RAG tuning and fine-tuning. Five domains, and the generative half is roughly as heavy as the classical one.
- Questions weighted to the official domain split, so a mock matches the real mix
- Every item tagged to the domain and subskill it tests
- Readiness shown against 700, not as an invented percentage
Free practice across all five domains. No card required.
AI-300Implement machine learning model lifecycle and operationsHard
A fraud-detection model is serving live traffic from a managed online endpoint. The team has trained version 4 and wants to release it without exposing every request to it at once. They also want retraining to start automatically if the distribution of incoming transactions moves away from the training data.
Which two actions should you take? Select two.
Two choices only. Your earliest pick was dropped.
0 of 2 selected
An original item written against the skills outline. Not a real exam question.
DP-100 to AI-300
Same models. The half of the job that happens after they work.
DP-100 tested whether you could build and train a model on Azure — experimentation, notebooks, hyperparameters, evaluation. AI-300 assumes that part and tests everything downstream: getting the model registered and versioned, deploying it behind an endpoint, rolling it out without breaking production, watching for drift, and retraining when it moves.
Then it adds a second half that DP-100 never touched. Roughly half of AI-300 is generative AI operations — Foundry infrastructure, prompt version control, groundedness and safety evaluation, RAG tuning, fine-tuning. If your Azure ML background is a few years old, that's where the work is.
| DP-100 | AI-300 | |
|---|---|---|
| Credential | Azure Data Scientist Associate | Machine Learning Operations Engineer Associate |
| Focus | Training and evaluating models | Running models and generative AI in production |
| Tooling | Azure Machine Learning | Azure ML, Microsoft Foundry, GitHub Actions, Bicep, Azure CLI |
| New ground | — | GenAIOps, evaluation metrics, RAG optimisation, fine-tuning |
Your Azure Machine Learning knowledge carries. What doesn't carry is the infrastructure-as-code work and the entire generative AI half — and together those are most of the paper.
Three exams
AI-103 builds it. AI-200 hosts it. AI-300 runs it.
Three Azure AI exams arrived close together and the codes don't explain themselves. The line between them is about which part of the system you own.
AI-103 is the AI itself: model choice, agents, retrieval design, prompt and evaluation work. AI-200 is the application around it: containers, vector storage, messaging, secrets and telemetry. AI-300 is the lifecycle of the model — training pipelines, deployment strategy, drift, evaluation and tuning, for classical ML and generative systems alike.
AI-200 and AI-300 look closest, and the clean way to separate them is this: AI-200 never asks you to train anything. AI-300 is full of it.
| AI-103 | AI-200 | AI-300 | |
|---|---|---|---|
| Tests | Building AI apps and agents | Hosting and operating AI apps | Operating models and GenAI systems |
| Heaviest domain | Generative AI and agentic solutions | Azure data management services | ML model lifecycle and operations |
| Model training | No | No | Yes |
| Take it if | You design the AI behaviour | You build and run the back end | You own the model after it ships |
None is a prerequisite for the others. If you came from a data science role, AI-300 is the continuation of your path.
Scoring
700 is a pass. It isn't 70%.
Microsoft reports technical exams on a scale of 1 to 1,000, and 700 or above passes. It's a scaled score, so it doesn't correspond to answering 70% of questions correctly — how many you need depends on the difficulty of the questions you're given.
No penalty for a wrong answer, so never leave one blank. Some questions are worth more than one point, and on some exams you can't return to a question once you've moved past it.
AI-300 · How Microsoft scales a score1–1,000 · 700 to pass
You answered
72%of the questions correctly
700
Your scaled score lands somewhere around670–730Could go either way
Now change nothing except which form you were given
Same answers. Harder form, higher scaled score — that's what scaling is for. It's also why nobody can tell you the percentage you need.
Positions here are illustrative. Microsoft publishes the 1–1,000 scale and the 700 pass mark, but not the conversion from raw answers — which is the point. When a number can't be known exactly, a range is the honest way to report it.
The loop
Practice, sit, review, repeat
Step 1 is free. Steps 2 to 4 need a subscription.
certs.acedify.com/ai-300
Practice · untimed
HardA fraud-detection model is serving live traffic from a managed online endpoint. The team has trained version 4 and wants to release it without exposing every request to it at once. They also want retraining to start automatically if the distribution of incoming transactions moves away from the training data.
Which two actions should you take? Select two.
Deploy version 4 alongside the current deployment on the same endpoint and route a small share of traffic to it before increasing it.
Increase the endpoint's instance count so latency stays flat while version 4 is released.
Configure a data drift monitor on the production inputs with a threshold that triggers the retraining pipeline.
Register version 4 in a separate workspace registry so the two versions can't conflict.
Progressive rollout on the same endpoint limits who sees version 4. A data-drift monitor with a retraining trigger is how the outline asks you to react when production inputs move. Domain: Implement machine learning model lifecycle and operations.
Click a step to see it.
What you get back
Not a prediction. A distance from the line.
Your readiness is a range, because it's an inference from the questions you've answered rather than a result you've earned. For a certification the outcome is binary, so the only question that matters is whether the whole range sits above 700.
While it straddles the line, you're a coin flip, and we'll say so.
AI-300 · 4 mocks completed
Where you stand
Borderline
530–720
Pass mark 700
Your range crosses the pass mark. On today's evidence it's a coin flip.
Three states
Try the three states. We won't show you a percentage chance of passing — nobody can calculate one honestly without years of real outcomes to calibrate against.
We won't show you a percentage chance of passing. Nobody can calculate one honestly without years of real outcomes behind it.
Domains
What AI-300 actually covers
Microsoft publishes a weighted skills outline. Our mocks follow those weights, so a domain worth a quarter of the exam gets a quarter of the questions.
| Domain | Weight |
|---|---|
| Design and implement an MLOps infrastructure | 15–20% |
| Implement machine learning model lifecycle and operations | 25–30% |
| Design and implement a GenAIOps infrastructure | 20–25% |
| Implement generative AI quality assurance and observability | 10–15% |
| Optimize generative AI systems and model performance | 10–15% |
Weights are ranges because Microsoft publishes them as ranges.
The shape here is different from AI-200's flat spread. Model lifecycle is the single heaviest domain and the one closest to what a data scientist already does, which makes it the easiest to over-prepare and feel ready on.
The three generative AI domains together run to somewhere between 40 and 55% of the paper. Two of them are small individually — 10 to 15% each — which makes them look skippable. They aren't, because between them they cover evaluation, observability, RAG tuning and fine-tuning, and none of that appears anywhere in a classical ML background.
Exam facts
Prices vary by country. Check Microsoft Learn for yours.
- Exam
- AI-300
- Full title
- Operationalizing Machine Learning and Generative AI Solutions
- Credential
- Machine Learning Operations Engineer Associate
- Pass mark
- 700 on a 1–1,000 scale
- Domains
- 5
- Renewal
- Annually, via a free online assessment on Microsoft Learn
- Length
- 120 minutes
- Delivery
- Pearson VUE
Pricing
Practice free. Pay when you want to know where you stand.
Pro
- Timed mocks at exam length
- A scaled score against the 700 pass mark
- Domain-weighted readiness, and your range over time
- Everything on every exam, one subscription
One subscription covers the Microsoft certifications including AI-300, AWS, the Claude certifications, TOEFL, IELTS and the SAT.
The exam fee is separate and goes to Microsoft. We're prep, not registration.
Start practicing for freeFAQ
AI-300 questions
Find out which domain is keeping you under 700.
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Start practicing for freeAlso available for AI-901, AI-200, AI-103, AZ-104 and the Claude certifications on the same subscription.