170 practice questions for Microsoft Certified: Machine Learning Operations (MLOps) Engineer Associate (AI-300), with full explanations.
Every question comes with the correct answer, the reasoning behind it, and a short note on why each wrong option is wrong. Work through it once with the answers, then again with the questions-only copy under exam conditions.
- 170 questions mapped to the AI-300 exam objectives
- Answers and explanations for every question, including the wrong options
- A questions-only PDF for timed practice runs
- Instant delivery by email the moment you check out
- Free monthly updates for as long as the exam is live
- Pass or your money back
An AI-300 attempt costs US$165 in the US. This pack is US$39, paid once.
Try 10 questions free before you buy.
Last updated September 2026 · 170 questions
What makes the AI-300 hard
AI-300 is a new exam, not a rename. It is an operations exam that assumes a model can already be trained; what it tests is whether it can be run in production, and then whether the same can be done for a generative AI application.
The first half is classic MLOps on Azure Machine Learning: workspaces, datastores, compute targets, registries, MLflow tracking, AutoML, hyperparameter sweeps, pipelines, managed endpoints, and data drift and retraining triggers. The second half is GenAIOps in Microsoft Foundry: serverless API endpoints versus managed compute, provisioned throughput units, prompt versioning in Git, evaluation with groundedness, relevance, coherence and fluency metrics, tracing and token-cost monitoring, and RAG tuning with chunk sizes, hybrid search and embedding fine-tuning. Bicep, Azure CLI and GitHub Actions run through every domain, so provisioning the infrastructure is expected alongside using it.
Because the exam is only a few months old, there is very little in circulation. This pack has 170 practice questions for the AI-300.
About the exam
AI-300 leads to Microsoft Certified: Machine Learning Operations (MLOps) Engineer Associate. It covers MLOps infrastructure and model lifecycle on Azure Machine Learning, GenAIOps infrastructure in Microsoft Foundry, generative AI evaluation and observability, and RAG and fine-tuning optimisation. Associate level, no prerequisites.
Exam domains
- Design and implement an MLOps infrastructure: 15 to 20%
- Implement machine learning model lifecycle and operations: 25 to 30%
- Design and implement a GenAIOps infrastructure: 20 to 25%
- Implement generative AI quality assurance and observability: 10 to 15%
- Optimize generative AI systems and model performance: 10 to 15%
Around 40 to 60 questions, pass mark 700 out of 1000, US$165 in the US, priced by local currency elsewhere, online proctored or test centre, renews annually for free.
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