AI-300 Practice Tests: Operationalizing ML and GenAI, 250 Qs
AI-300 Practice Tests: Operationalizing ML and GenAI, 250 Qs
Are you ready for AI-300? These five timed practice tests tell you, domain by domain, before you book the exam.
AI-300, Operationalizing Machine Learning and Generative AI Solutions, is the exam for the Microsoft Certified: Machine Learning Operations Engineer Associate. This practice set contains 250 original questions written against the official AI-300 study guide and checked against current Microsoft Learn documentation, including the Microsoft Foundry naming and role names.
AI-300 replaced DP-100, which retired on June 1, 2026. The model lifecycle is still there, but the exam now expects you to think like an operations engineer: infrastructure as code with Bicep and the Azure CLI, GitHub Actions with OIDC, managed networks and private endpoints, managed identities and RBAC. Three domains, together 40 to 55 percent of the exam, cover generative AI operations on Microsoft Foundry: deployment types and provisioned throughput, prompt versioning, evaluation and safety, tracing and cost, RAG tuning and fine-tuning. DP-100 practice material prepares you for the wrong exam.
The five AI-300 tests build up in difficulty:
- Test 1, Groundwork: core concepts, one idea per question.
- Test 2, In Practice: short scenarios that separate features that are easy to confuse.
- Test 3, Trade-offs: two answers look right, one constraint decides.
- Test 4, Under the Hood: CLI, SDK, YAML, settings and limits.
- Test 5, Exam Day: a full-length simulation with long, multi-constraint scenarios.
Every answer option has its own feedback, so you learn why a wrong answer is wrong and what it actually is. Every question also has an overall explanation with a link to the Microsoft Learn page it is based on.
Each test has 50 questions spread over the five exam domains in the official weighting, and PrepKiwi reports your score per domain after each attempt. The pass mark is 80 percent for Tests 1 to 4 and 85 percent for Test 5, higher than the real exam on purpose.
What this practice set is not: it is not a video course and not a copy of real exam questions. It is a set of practice exams that test and explain. Use it alongside hands-on work in Azure and the free Microsoft Learn training for AI-300.
Groundwork: MLOps and GenAIOps Core Concepts
50 questions · 80 minutes · pass mark 80%
Start here, and take this test cold as your baseline. Test 1 covers the full AI-300 skills outline at foundation level: Azure Machine Learning workspaces, datastores, compute and environments, MLflow tracking, endpoints and monitoring, Microsoft Foundry resources, deployment types and access control, evaluation metrics, tracing, RAG basics and fine-tuning methods. Each question checks one concept with a short stem, so you quickly see which domains you already know and which need work. 50 questions, 6 of them multi-select, spread across all five exam domains in the official weighting. You have 80 minutes and need 80% to pass. That bar is deliberately higher than the real exam's scaled 700, because a practice test at home is easier than a proctored exam and you want a margin. Every answer option has its own feedback, and every question ends with an explanation and a link to Microsoft Learn.
In Practice: Azure ML and Foundry Scenarios
50 questions · 85 minutes · pass mark 80%
Test 2 moves from definitions to short practical scenarios. You are an MLOps engineer with a workspace, a pipeline or a Foundry project, and you must pick the right feature, role, command or setting. Many questions put two neighbouring options side by side: identity-based versus credential-based datastores, mirrored traffic versus a traffic split, data drift versus prediction drift, Standard versus Data Zone deployments, AI-assisted versus NLP evaluators. 50 questions, 7 multi-select, same domain weighting as the real exam. You have 85 minutes and need 80% to pass. Review the per-option feedback on every question, including the ones you got right, and check your score per domain before moving on to Test 3.
Trade-offs: When Two Answers Look Right
50 questions · 80 minutes · pass mark 80%
Test 3 is about judgement. In most questions two answers look reasonable, and one constraint in the scenario decides: data residency, cost, latency, on-premises hosting, data exfiltration, auditing or the absence of ground truth. You choose between AutoML and a sweep job, PTU and pay-per-token, Global Batch and Standard, HNSW and exhaustive KNN, RAG and fine-tuning, local and cloud evaluation. 50 questions, 7 multi-select, longer stems than Tests 1 and 2. You have 80 minutes and need 80% to pass. If you miss a question here, read which constraint you overlooked; that habit is what the real exam rewards.
Under the Hood: CLI, SDK, YAML and Limits
50 questions · 65 minutes · pass mark 80%
Test 4 goes into implementation detail: CLI v2 commands, SDK v2 and YAML syntax, deployment settings, HTTP status codes, limits and the order of steps. Expect questions on az ml and az cognitiveservices commands, sweep search spaces, pipeline bindings, traffic and mirroring rules, batch deployment settings, provisioned throughput sizing and spillover, private networking, evaluator scales, fine-tuning hyperparameters, checkpoints and deployment lifetimes. 50 questions, 8 multi-select. The stems are shorter but the answers are precise, so the clock is tighter: 65 minutes, 80% to pass. Keep Microsoft Learn open while you review the explanations; every question links to the page it is based on.
Exam Day: Full-Length AI-300 Simulation
50 questions · 75 minutes · pass mark 85%
Test 5 is your readiness check. Take it last, in one uninterrupted sitting. The questions are the longest in this practice set, with several constraints per scenario and all five domains mixed, as on exam day. 50 questions, 8 multi-select. You have 75 minutes and need 85% to pass. That is stricter than the real exam on purpose: the real exam gives more time per question but also includes case studies and other interactive item types, and a scaled 700 is not the same as 70%. If you pass this test at 85% without guessing, you are in a strong position. If you do not, go back to your weakest domain, reread the explanations and retake the test after a few days.
What you will practice
- Test your readiness for AI-300 with 250 questions across all five domains, weighted like the official skills outline
- Build, deploy and monitor ML models in Azure Machine Learning: pipelines, MLflow, endpoints, safe rollout and drift
- Choose the right Foundry deployment type, access role, network setup and PTU plan for GenAI workloads under constraints
- Evaluate, trace and optimize GenAI apps: quality and safety evaluators, Azure Monitor metrics, RAG tuning, fine-tuning
- Learn from feedback on every answer option plus an explanation with a Microsoft Learn link for every question
- Progress from core concepts through scenarios, trade-offs and implementation detail to a timed exam simulation
- Find your weak domain quickly with per-domain score reporting after each attempt
- Recognise the constraints that decide Microsoft exam questions: residency, cost, latency, security and data
Exam domains covered
Who is this for
This practice set is for anyone preparing for Microsoft exam AI-300, Operationalizing Machine Learning and Generative AI Solutions, which leads to the Microsoft Certified: Machine Learning Operations Engineer Associate certification. It fits data scientists who want to take models to production, DevOps and platform engineers moving into MLOps and GenAIOps, cloud engineers who support AI teams on Azure, and students or career changers who have worked through the Microsoft Learn material and want to know if they are ready. Solution architects and technical leads who need to understand how Azure Machine Learning and Microsoft Foundry are run in production will also find the scenarios useful. If you studied for DP-100, note that DP-100 retired on June 1, 2026. AI-300 keeps the model lifecycle, but adds infrastructure as code, GitHub Actions, networking and identity, and three generative AI domains on Foundry deployment, evaluation, observability, RAG and fine-tuning. Old DP-100 practice material will leave gaps. This practice set is a set of timed practice exams with detailed explanations. It tests and explains; it does not replace hands-on experience or a full training course.
Requirements
- Basic familiarity with Azure and Python. The exam profile assumes Python, the Azure CLI and basic DevOps with GitHub Actions; beginners can start with Test 1.
- Some hands-on time in Azure Machine Learning and Microsoft Foundry helps a lot. A free or pay-as-you-go Azure subscription is enough to follow along.
- These are practice exams, not a video course. Use them alongside the free official Microsoft Learn paths for AI-300, not instead of them.
Free exam guide
Not sure what to expect? Read how the exam is built, what it covers and how to prepare.