AI-300 Exam Questions & Answers 2026 — Set 04

Practice AI-300 questions 124–155. This standalone set contains 32 independently created practice questions for the Microsoft AI-300: Operationalizing Machine Learning and Generative AI Solutions exam, covering Azure Machine Learning, MLOps, GenAIOps, Microsoft Foundry, model training and deployment, MLflow, infrastructure as code, generative AI evaluation, observability, RAG optimization, fine-tuning, and production AI operations.

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