AI-300 Operationalizing Machine Learning and Generative AI Solutions

600 AI-300 practice test questions across MLOps, GenAIOps, evaluation and fine-tuning. Explanations that teach the topic, never the answer. 40 free.

600 exam-style questions for AI-300, the Microsoft MLOps Engineer Associate exam, across all five skill areas. Every answer is explained and cited, and 40 questions are free.

AI-300 is Microsoft's exam for the Machine Learning Operations Engineer Associate credential, and it replaced DP-100 when that exam retired in June 2026. It is the paper for people who must keep machine learning and generative AI running in production: the workspaces and projects underneath it, the pipelines that retrain it, the evaluation that says whether it is still good, and the bills it generates. This bank holds 600 questions written against the objectives published for the exam, everyone carrying an explanation and a citation to the Microsoft document it was written from.

What is covered:

  1. Design and implement an MLOps infrastructure - 114 questions. Azure Machine Learning workspaces and Microsoft Foundry projects, the associated resources they provision, compute instances and clusters, datastores and data assets, environments, identity and role assignments, managed networking, and infrastructure as code with Bicep and the command line.
  2. Implement machine learning model lifecycle and operations - 174 questions. Experiment tracking with MLflow, automated machine learning, online and batch endpoints, staged rollout with traffic splitting, model monitoring and its drift signals, automated retraining, and the pipelines that promote a model from development to production.
  3. Design and implement a GenAIOps infrastructure - 144 questions. The Foundry resource and its projects, the model catalog and model lifecycle, deployment types and provisioned throughput, capacity and reservations, private networking, agents and tools, and migration away from prompt flow.
  4. Implement generative AI quality assurance and observability - 84 questions. Evaluation datasets and evaluators, judge models, groundedness and relevance, content safety severity, custom evaluators, tracing with OpenTelemetry, the agent monitoring dashboard, continuous evaluation, and cost analysis.
  5. Optimize generative AI systems and model performance – 84 questions. Chunking and overlap, similarity thresholds, hybrid search and rank fusion, semantic reranking, embedding model selection and vector compression, supervised and preference fine-tuning, reinforcement fine-tuning and its graders, and synthetic training data.

Written as an operations exam, not a data science exam:

The candidate profile assumes you can already train a model. What AI-300 tests are what you do with it afterward: which project shape to build on, which deployment type a residency requirement force, what a retirement date means for a running application, why an evaluation returned no rows, and where the money is going. Preparing for it by revising algorithms and hyperparameters is the most common way to walk in underprepared, and it is why a data science background on its own is not enough. The questions here ask about what to operate and what to choose.

Explanations that teach the topic:

Every explanation is written to survive being read after you got the question wrong. It names the distinction the question turned on, says where the boundary sits, and names the single confusion most people bring rather than walking through every option in turn. It ends with the Microsoft document it was written from, so you can go and read the source. No explanation restates the winning option, and 84 distinct Microsoft documents are cited across the 600 questions.

Scenario questions with one decision:

There are no case studies to read here and effectively no code to trace. Each question puts you in a stated situation - a team, a constraint, a symptom - and asks for one decision. The four options are deliberately close, differing on the single boundary the question is about, which is the shape the real paper favors and the shape that exposes whether you know a rule or only recognize a term.

Exam Coach AI, besides every question:

Exam Coach AI sits next to the question while you work. Ask it what a term means, when to use one service instead of another, or why a setting exists, and it answers in a sentence or two without touching your exam. It will not tell you which option to pick and it will not confirm an answer, deliberately - it is there to close the gap in understanding that made the question hard, not to get you past it.

Coming from AI-103:

If you have sat AI-103, you already know Microsoft Foundry, agents, retrieval and evaluation from the developer side. AI-300 takes the same services and asks the operator's questions about them: what happens when the model you built on reaches its retirement date, who is paying for a deployment nobody is calling, why your evaluation scores stopped appearing halfway through an hour. The overlap is real enough that AI-103 is the natural exam to sit first, and the AZ-400 DevOps bank covers the pipeline and release-gate practice that the first two domains here assume you already have.

How it is marked:

You need 70% of the questions you are given, marked as a straight percentage of your own paper. Sixty questions are drawn per sitting from a pool of 600, weighted the way the objectives are: 114 on MLOps infrastructure, 174 on the model lifecycle, 144 on GenAIOps infrastructure, 84 on quality and observability, and 84 on optimization. Your result breaks down by skill area, so a weak domain is visible rather than averaged away.

Free preview:

AI-300 Practice test 40 questions are free, spread across all five skill areas rather than clustered in the easy one: eight on MLOps infrastructure, eleven on the model lifecycle, nine on GenAIOps infrastructure, six on quality and observability, and six on optimization. They are ordinary questions from the bank with their full explanations, not a trailer.

Explore PrepifyLabs

Certification practice exams

Insights and case studies

The most advanced examination and certification readiness platform available. Train autonomously, pass effortlessly.

Prepifylabs LLC, 5900 Balcones Drive STE 38508, Austin, TX 78731, United States