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Microsoft AI-300 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: 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. Manage compute targets, datastores, and environments
        • 2. Configure workspace settings and security
          Topic 2: 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
                  Topic 3: Optimize generative AI systems and model performance15–20%- Improve efficiency and cost-effectiveness
                  • 1. Manage resource utilization
                    • 2. Optimize inference and deployment
                      - Optimize model selection and configuration
                      • 1. Choose appropriate models and parameters
                        • 2. Tune prompts and generation settings
                          Topic 4: Design and implement a GenAIOps infrastructure20–25%- Implement infrastructure for generative AI workloads
                          • 1. Integrate with Azure services and tools
                            • 2. Design scalable and secure architecture
                              - Set up Microsoft Foundry environment
                              • 1. Manage compute and deployment resources
                                • 2. Configure projects, connections, and security
                                  Topic 5: Implement machine learning model lifecycle and operations25–30%- Deploy models to production
                                  • 1. Deploy to real-time and batch endpoints
                                    • 2. Configure deployment options and scaling
                                      - 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

                                                  Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:

                                                  You create an Azure Machine Learning workspace.
                                                  You must use the Python SDK v2 to implement an experiment from a Jupyter notebook in the workspace. The experiment must log string metrics. You need to implement the method to log the string metrics. Which method should you use?

                                                  • A. mlflow.log.dict()
                                                  • B. mlflowlog_metrk()
                                                  • C. mlflow.log text()
                                                  • D. mlflow.log_artifact()
                                                  Reveal Solution  Discussion  0

                                                  Correct Answer: C  🗳️

                                                  You create a workspace by using Azure Machine Learning Studio.
                                                  You must run a Python SDK v2 notebook in the workspace by using Azure Machine Learning Studio.
                                                  You need to reset the state of the notebook.
                                                  Which three actions should you use? Each correct answer presents a complete solution.

                                                  • A. Stop the current kernel.
                                                  • B. Change the current kernel.
                                                  • C. Navigate to another section of the workspace.
                                                  • D. Change the compute.
                                                  • E. Reset the compute.
                                                  Reveal Solution  Discussion  0

                                                  Correct Answer: B,D,E  🗳️

                                                  Explanation: Only visible for PassLeaderVCE members. You can sign-up / login (it's free).

                                                  You train and publish a machine teaming model.
                                                  You need to run a pipeline that retrains the model based on a trigger from an external system.
                                                  What should you configure?

                                                  • A. Azure Batch
                                                  • B. Azure Data Catalog
                                                  • C. Azure logic App
                                                  Reveal Solution  Discussion  0

                                                  Correct Answer: C  🗳️

                                                  You need to standardize how Fabrikam Inc. manages machine learning assets.
                                                  Which action should you perform first?

                                                  • A. Deploy a managed online endpoint.
                                                  • B. Register assets in the Azure Machine Learning registry.
                                                  • C. Create a new Microsoft Foundry project.
                                                  • D. Create a shared Azure Machine Learning workspace.
                                                  Reveal Solution  Discussion  0

                                                  Correct Answer: D  🗳️

                                                  Explanation: Only visible for PassLeaderVCE members. You can sign-up / login (it's free).

                                                  you create an Azure Machine learning workspace named workspace1. The workspace contains a Python SOK v2 notebook mat uses Mallow to correct model coaxing men's anal arracks from your local computer.
                                                  Vou must reuse the notebook to run on Azure Machine I earning compute instance m workspace.
                                                  You need to comminute to log training and artifacts from your data science code.
                                                  What should you do?

                                                  • A. Log in to workspace1.
                                                  • B. Instantiate the MLClient class.
                                                  • C. Configure the tracking URL.
                                                  • D. Instantiate the job class.
                                                  Reveal Solution  Discussion  0

                                                  Correct Answer: C  🗳️

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