Free practice questions covering Azure ML, MLOps pipelines, model deployment, RAG patterns, prompt engineering, and AI solution monitoring. Aligned with the official Microsoft study guide.
The AI-300 (Operationalizing Machine Learning and Generative AI Solutions) is a Microsoft certification exam for professionals who design, deploy, and manage ML and GenAI solutions on Azure. It validates skills in MLOps, model lifecycle management, and production AI systems.
| Exam Code | AI-300 |
| Full Name | Operationalizing Machine Learning and Generative AI Solutions |
| Provider | Microsoft |
| Prerequisite | None (AI-102 or Azure ML experience recommended) |
| Question Format | Multiple choice, case studies, drag-and-drop, hot area |
| Passing Score | 700 out of 1000 |
| Duration | 120 minutes |
| Exam Fee | $165 USD |
Azure AI services architecture, solution requirements analysis, responsible AI principles, cost optimization, Azure OpenAI Service planning, model selection strategy, compute and infrastructure planning, security and compliance for AI.
Data ingestion pipeline design, feature engineering, data validation and quality, Azure Data Factory for ML, data versioning and lineage, real-time vs batch processing, data labeling and annotation, Azure Databricks integration.
Azure Machine Learning workspace, training compute management, hyperparameter tuning, Automated ML (AutoML), distributed training, model evaluation and validation, MLflow experiment tracking, fine-tuning foundation models, prompt engineering for GenAI, RAG pattern implementation.
Model deployment strategies, Azure ML managed endpoints, real-time vs batch inference, model packaging and containerization, blue-green and canary deployments, auto-scaling inference endpoints, Azure Kubernetes Service for ML, edge deployment with ONNX.
Model monitoring and drift detection, data drift vs concept drift, Azure ML model monitoring, logging and alerting for AI systems, model retraining triggers, A/B testing for models, cost monitoring for AI workloads, incident response for AI failures.
Professionals building and deploying ML pipelines on Azure who want to validate their production ML skills.
Those designing end-to-end AI systems including data pipelines, training infrastructure, and deployment strategies.
Data scientists transitioning from notebooks to production-grade ML systems with monitoring and governance.
Engineers working with Azure OpenAI, RAG patterns, fine-tuning, and prompt engineering in enterprise environments.
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