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Microsoft AI-300 Study Guide

Microsoft Machine Learning Operations Engineer AI-300

Microsoft AI-300, Operationalizing Machine Learning and Generative AI Solutions, earns the Machine Learning Operations Engineer Associate certification. It covers MLOps with Azure Machine Learning and GenAIOps with Microsoft Foundry: infrastructure, the model lifecycle, generative AI evaluation and observability, and optimization. It suits data scientists and engineers who deploy and run AI solutions in production.

  • Exam code AI-300
  • Associate level
  • 5 exam domains

This guide is free, with no sign-up. It explains every Microsoft AI-300 exam topic in plain English, one chapter at a time. Read them in order or jump straight to the topic you need.

  • 5chapters
  • 19topics
  • 5diagrams and exhibits
  • 1 h 33 minfast track
Start with Design and Implement an MLOps Infrastructure
  • Every topic follows the same simple pattern: what it is, how it works, common mistakes
  • Diagrams and real command output where a picture explains it best
  • Exam tips and worked subnetting and IPv6 practice, with answers

How the exam is weighted

Each domain is worth roughly this share of the exam. The exam provider publishes a range for each domain, so the bar marks the middle of it. Spend your study time in proportion, and select a domain to open its chapter.

Chapters

Open a chapter to see its topics and how long each takes.

1Design and Implement an MLOps Infrastructure4 topics · 19 min fast trackMachine Learning workspaces, datastores, compute, assets, registries, and infrastructure as code with Bicep and GitHub.
  1. 1Workspaces, Datastores, Compute and Access5 min
  2. 2Data Assets, Environments, Components and Registries5 min
  3. 3GitHub Access, Bicep and GitHub Actions Automation5 min
  4. 4Network Isolation and Git Source Control4 min
Read the chapter overview →
2Implement Machine Learning Model Lifecycle and Operations5 topics · 23 min fast trackTracking and training models, pipelines, registration, responsible AI, endpoints, safe rollout, drift monitoring, retraining.
  1. 1MLflow Tracking, AutoML, Notebooks and Comparing Jobs4 min
  2. 2Training Jobs, Sweeps, Distributed Training and Pipelines4 min
  3. 3Model Registration, Responsible AI and Lifecycle5 min
  4. 4Online and Batch Endpoints, Testing and Safe Rollout5 min
  5. 5Drift Detection, Production Monitoring and Retraining Triggers5 min
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3Design and Implement a GenAIOps Infrastructure4 topics · 20 min fast trackFoundry resources, identity, private networking, Bicep, model deployments, provisioned throughput and prompt versioning.
  1. 1Foundry Resources, Projects and Access Control5 min
  2. 2Private Networking and Infrastructure as Code for Foundry4 min
  3. 3Model Deployments, Selection, Versioning and Provisioned Throughput6 min
  4. 4Prompts, Variants and Git Version Control5 min
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4Implement Generative AI Quality Assurance and Observability3 topics · 15 min fast trackEvaluation datasets, quality and safety evaluators, automated evaluation, Foundry monitoring, tracing and token cost.
  1. 1Test Datasets, Data Mapping and Quality Metrics5 min
  2. 2Safety Evaluations and Automated Evaluation Workflows5 min
  3. 3Monitoring, Tracing, Performance and Cost5 min
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5Optimize Generative AI Systems and Model Performance3 topics · 16 min fast trackTuning RAG retrieval, embeddings, hybrid search, A/B testing, and fine-tuning with synthetic data through to production.
  1. 1Retrieval Tuning and Embedding Models5 min
  2. 2Hybrid Search and RAG Evaluation with A/B Testing5 min
  3. 3Fine-Tuning Methods, Synthetic Data and Production Deployment6 min
Read the chapter overview →

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