Free AI-300 Exam Prep
Exam AI-300: Operationalizing Machine Learning and Generative AI Solutions
Prepare for the AI-300 exam without spending hundreds on expensive prep courses. Free study guides, practice questions, flashcards, and related exam resources.
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AI-300 Microsoft Azure Certifications License: Complete Roadmap
Follow this path to organize your licensing and exam preparation
Phase 1: MLOps InfrastructureYou are here
Provision Azure ML workspaces, datastores, compute, registries, and network security with Bicep, Azure CLI, and GitHub Actions.
Phase 2: Model Lifecycle and Operations
Train with MLflow, AutoML, sweeps, and distributed PyTorch; register and deploy models to online and batch endpoints; monitor drift.
Phase 3: GenAIOps Infrastructure
Build Foundry hubs and projects, deploy foundation models, configure connections and managed identities, and version prompts with Git.
Phase 4: Quality, Safety, and Observability
Use built-in and custom evaluators, configure continuous evaluation, distributed tracing, latency, token, and cost metrics.
Phase 5: Optimization and Timed Review
Tune RAG, choose embedding models, fine-tune with LoRA, generate synthetic data, and complete timed practice exams.
Can You Take the AI-300 Exam?
Check if you meet the basic eligibility requirements
- •Exam is proctored and may include scenario-based and interactive question types
- •Passing score is 700
- •Associate certification requires annual renewal on Microsoft Learn at no cost
AI-300 Quick Facts
Time to Get Licensed
6-10 weeks for most candidates with Azure ML and DevOps experience
From start to license in hand
Retake Policy
After a first failed attempt, retake is allowed after 24 hours. If you fail a second time, wait 14 days between subsequent retakes. Maximum five attempts in a 12-month period.
Total Cost Breakdown
Free AI-300 Prep Tools
Reported exam pass rate: Microsoft does not publish public exam-level pass-rate percentages.. Check the exam sponsor for the latest official figure.
100 Practice Questions
AI-300-aligned questions across the five current Microsoft skills areas.
AI-Powered Learning
Identify weak spots in Azure ML, Foundry, MLflow, Bicep, GitHub Actions, and evaluation.
2026 Updated
Aligned to the AI-300 study guide last updated in 2026.
Free Access
Free AI-300 practice questions with no signup required.
What You'll Study
16 chapters covering the exam topics in this guide
Introduction & Exam Overview
3 sections
Chapter 2: Azure Machine Learning Workspaces, Datastores, Compute, and IAM
4 sections
Chapter 3: Azure Machine Learning Data Assets, Environments, Components, and Registries
4 sections
Chapter 4: Infrastructure as Code, GitHub, and Network Isolation for Azure ML
4 sections
Chapter 5: MLflow Tracking, AutoML, Training Jobs, and Hyperparameter Sweeps
4 sections
Chapter 6: Distributed Training, Training Pipelines, and Job Comparison
3 sections
Chapter 7: Model Registration, Responsible AI, and Lifecycle Management
3 sections
Chapter 8: Online Endpoints, Batch Endpoints, Rollout, and Rollback
3 sections
Chapter 9: Data Drift, Production Metrics, Retraining, and Alerts
2 sections
Chapter 10: Microsoft Foundry Resources, IAM, Networking, and IaC
4 sections
Chapter 11: Deploying and Managing Foundation Models
4 sections
Chapter 12: Prompt Design, Variants, and Git Version Control
3 sections
Chapter 13: Evaluation Datasets, Quality Metrics, Safety, and Automated Workflows
4 sections
Chapter 14: Continuous Monitoring, Performance, Cost, Tracing, and Logging
3 sections
Chapter 15: Retrieval Tuning, Embeddings, Hybrid Search, and RAG Evaluation
4 sections
Chapter 16: Fine-Tuning Methods, Synthetic Data, and Promotion to Production
3 sections
AI-300 Exam Details
Exam AI-300: Operationalizing Machine Learning and Generative AI Solutions
Administered by Microsoft
Exam Content Breakdown
Based on the official Microsoft content outline
Workspaces, datastores, compute targets, environments, components, registries, IaC with Bicep and Azure CLI, GitHub Actions, identity, and network security.
MLflow tracking, AutoML, hyperparameter sweeps, distributed training, pipelines, model registration, responsible AI, online and batch endpoints, and drift monitoring.
Microsoft Foundry hubs and projects, managed identities, private networking, foundation model deployments, PTUs, and prompt versioning with Git.
Built-in and custom evaluators, risk and safety evaluation, continuous monitoring, distributed tracing, performance and cost metrics, and detailed logging.
RAG tuning, hybrid and semantic search, embedding selection, LoRA and full fine-tuning, synthetic data generation, and fine-tuned model promotion.
What's Included
16 Chapters
Complete exam coverage
Practice Quizzes
With detailed explanations
Free to Start
No credit card required
Quality Exam Prep Shouldn't Cost Hundreds
I'm Ran Chen, an engineer with 20+ years of coding experience. I passed my Life Insurance license, EA exam, SIE, Series 6, 63, 65, and finally the CFP® exam.
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What's Next After the AI-300?
After passing the AI-300, you can pursue these career paths
AI-300 Exam FAQ
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