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Microsoft · Operationalizing Machine Learning and Generative AI Solutions
AI-300 Practice Questions & Answers 2026
Test your knowledge with 165 independently created practice questions organized into 5 standalone sets. Start with any set—there is no required order.
165Practice questions
5Standalone sets
AI-300Exam code
2026Series
5 VIDEO SETS
AI-300 practice sets
Each set has its own dedicated watch page so the video is the main content of that page.
What this series covers
MLOps architecture on AzureAzure Machine Learning workspacesAzure Machine Learning datastoresAzure Machine Learning compute targetsMachine Learning data assets and environmentsMachine Learning components and registriesIdentity and access management for Machine LearningInfrastructure as code with BicepAzure CLI for AI infrastructureGit and GitHub integrationGitHub Actions for MLOpsMLflow experiment trackingAutomated machine learningHyperparameter tuningDistributed model trainingMachine learning training pipelinesModel registration and versioningResponsible AI model evaluationReal-time model endpointsBatch model endpointsProgressive deployment and rollbackModel monitoring and performance metricsData drift detectionAutomated retraining and alertsGenAIOps architectureMicrosoft Foundry projects and environmentsManaged identities and RBACPrivate networking and AI workload securityFoundation model deploymentServerless API endpointsManaged compute for foundation modelsModel selection and version managementProvisioned throughputPrompt engineering and prompt managementPrompt versioning with GitGenerative AI application evaluationGroundedness, relevance, coherence, and fluency metricsAI safety and harmful content evaluationAutomated generative AI evaluation workflowsGenerative AI observabilityLatency, throughput, and response-time monitoringToken consumption and cost monitoringLogging, tracing, and debuggingRetrieval-augmented generation optimizationEmbedding model optimizationVector similarity and retrieval strategiesHybrid semantic and keyword searchRAG relevance metrics and A/B testingAdvanced model fine-tuningSynthetic data for fine-tuningFine-tuned model deployment and lifecycle management
Exam-practice approach
These practice questions are independently created for educational and exam-preparation purposes and are not actual exam questions, dumps, leaked questions, or confidential certification content.