Microsoft AI-300最新試験情報 & AI-300サンプル問題集

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Microsoft AI-300 Exam Syllabus Topics:

SectionWeightObjectives
Implement machine learning model lifecycle and operations25–30%- Orchestrate model training and experimentation
  • 1. Track experiments and metrics
    • 2. Create and manage pipelines
      - Monitor and maintain models in production
      • 1. Monitor data and model drift
        • 2. Implement retraining and update workflows
          - Register, version, and package models
          • 1. Manage model registry
            • 2. Create reusable model packages
              - Deploy models to production
              • 1. Deploy to real-time and batch endpoints
                • 2. Configure deployment options and scaling
                  Optimize generative AI systems and model performance15–20%- Optimize model selection and configuration
                  • 1. Choose appropriate models and parameters
                    • 2. Tune prompts and generation settings
                      - Improve efficiency and cost-effectiveness
                      • 1. Manage resource utilization
                        • 2. Optimize inference and deployment
                          Design and implement a GenAIOps infrastructure20–25%- Implement infrastructure for generative AI workloads
                          • 1. Design scalable and secure architecture
                            • 2. Integrate with Azure services and tools
                              - Set up Microsoft Foundry environment
                              • 1. Configure projects, connections, and security
                                • 2. Manage compute and deployment resources
                                  Design and implement an MLOps infrastructure15–20%- Implement infrastructure as code for Machine Learning
                                  • 1. Use Bicep or Azure CLI to deploy resources
                                    • 2. Automate infrastructure provisioning
                                      - Create and manage Machine Learning workspace resources and assets
                                      • 1. Configure workspace settings and security
                                        • 2. Manage compute targets, datastores, and environments
                                          Implement generative AI quality assurance and observability10–15%- Evaluate and test generative AI applications
                                          • 1. Define evaluation metrics and criteria
                                            • 2. Test for safety, accuracy, and relevance
                                              - Monitor generative AI systems
                                              • 1. Implement logging and alerting
                                                • 2. Track usage, performance, and errors

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                                                  AI-300サンプル問題集、AI-300模擬試験

                                                  Tech4Examを通じて最新のMicrosoftのAI-300試験の問題と解答早めにを持てて、弊社の問題集があればきっと君の強い力になります。

                                                  Microsoft Operationalizing Machine Learning and Generative AI Solutions 認定 AI-300 試験問題 (Q50-Q55):

                                                  質問 # 50
                                                  You have an Azure Machine Learning workspace named Workspace