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Microsoft AI-300 Exam Syllabus Topics:

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

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                                                  Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions (Q99-Q104):

                                                  NEW QUESTION # 99
                                                  You have an Azure Machine Learning workspace named Workspace

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