Microsoft Machine Learning Operations Engineer (AI-300) ® 2026
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⚡ Quick Facts
Quick Answer: The AI-300 exam has 40-60 questions, a 100 minutes time limit, a 700 / 1000 passing score, and costs $165 USD. It covers MLOps Infrastructure Design, Model Lifecycle & Operations, GenAIOps Infrastructure, GenAI Quality Assurance & Observability. ExamCert has 1000+ free practice questions, a full study guide, and a money-back guarantee.
AI-300 Study Materials
AI-300 is Microsoft’s AI operations exam, covering MLOps and GenAIOps together as AIOps. It spans workspace and IaC setup, the full model lifecycle from training through drift monitoring, Foundry environments and foundation-model deployment, generative-AI evaluation and observability, and RAG and fine-tuning optimization. Passing earns the Machine Learning Operations Engineer Associate credential.
1000+ AI-300 Questions
Scenario-based questions covering MLOps Infrastructure Design, Model Lifecycle & Operations, GenAIOps Infrastructure, GenAI Quality Assurance & Observability, GenAI Optimization & Model Performance
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Every answer explained so you understand the why behind MLOps Infrastructure Design, Model Lifecycle & Operations, GenAIOps Infrastructure and more
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New questions added weekly, aligned to the current AI-300 exam objectives
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Study Tips from Top Scorers
- 1.Score 85%+ consistently on practice tests before booking your exam
- 2.Review explanations for BOTH correct and wrong answers
- 3.Use the Study Guide for quick revision 2-3 days before exam day
- 4.Focus extra time on domains where your scores are lowest
- 5.Take timed practice sets to build exam-day stamina
AI-300 Exam Domains
40-60 questions in 100 minutes. Passing score: 700 / 1000. Exam fee: $165 USD. Format: Multiple choice & interactive case items.
MLOps Infrastructure Design
Create and manage Azure Machine Learning workspaces, datastores, compute targets, data assets, environments, components, and registries. Implement infrastructure as code with Bicep and Azure CLI, wire up GitHub integration and Actions, restrict workspace network access, and manage projects with Git.
Model Lifecycle & Operations
Orchestrate training with MLflow experiment tracking, AutoML, hyperparameter tuning, distributed training, and training pipelines. Register and version MLflow models, evaluate with responsible-AI principles, deploy to real-time or batch endpoints, run progressive rollout and rollback, and monitor for data drift and retraining triggers.
GenAIOps Infrastructure
Create Microsoft Foundry resources and project environments, configure managed identities and RBAC, implement private networking, deploy foundation models via serverless API endpoints or managed compute, configure provisioned throughput units, and version prompts in Git with prompt variants.
GenAI Quality Assurance & Observability
Build test datasets and data mappings, apply AI quality metrics (groundedness, relevance, coherence, fluency), configure risk and safety evaluations for harmful content, automate evaluation workflows, and set up continuous monitoring for latency, throughput, token consumption, cost, tracing, and debugging.
GenAI Optimization & Model Performance
Tune RAG retrieval with similarity thresholds, chunk sizes, and hybrid semantic plus keyword search; select and fine-tune embedding models; A/B test relevance; implement advanced fine-tuning and synthetic data; and manage fine-tuned models from development into production.
Why AI-300?
AI-300 is Microsoft’s AI operations exam, covering MLOps and GenAIOps together as AIOps. A respected Microsoft credential for MLOps Engineer, Machine Learning Engineer, AI Platform Engineer.
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Who Is the AI-300 Exam For?
Target Audience
For MLOps and GenAIOps engineers who build the infrastructure behind AI — training, deploying, and maintaining traditional ML models in Azure Machine Learning, and deploying, evaluating, monitoring, and optimizing generative AI apps and agents in Microsoft Foundry.
Recommended Prerequisites
- •Data science background with Python programming experience
- •Entry-level DevOps understanding, including GitHub Actions and CLIs
- •Hands-on experience with Azure Machine Learning, Microsoft Foundry, and IaC using Bicep and Azure CLI
Official Exam Details
Format: Multiple choice & interactive case items
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What Our Users Say About AI-300
"The MLOps Infrastructure Design questions were exactly like the real AI-300 exam. Passed on my first attempt!"
"Model Lifecycle & Operations and GenAIOps Infrastructure questions were spot-on, and the clear explanations made the tough topics click."
"The realistic scenarios helped me master MLOps Infrastructure Design fast. Comprehensive and fully up to date for 2026."
"The GenAI Optimization & Model Performance questions filled the gaps in my knowledge. I scored well above the passing mark."
"GenAIOps Infrastructure questions felt realistic. The practice sets cover every AI-300 objective thoroughly."
"Best AI-300 prep app I found. Weekly updates kept everything aligned with the latest exam objectives."
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AI-300 2026
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