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Microsoft AI-300 Exam Syllabus Topics:

SectionWeightObjectives
Implement machine learning model lifecycle and operations25–30%- Register, version, and package models
  • 1. Create reusable model packages
    • 2. Manage model registry
      - 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
                  Optimize generative AI systems and model performance15–20%- Improve efficiency and cost-effectiveness
                  • 1. Optimize inference and deployment
                    • 2. Manage resource utilization
                      - Optimize model selection and configuration
                      • 1. Choose appropriate models and parameters
                        • 2. Tune prompts and generation settings
                          Implement generative AI quality assurance and observability10–15%- Monitor generative AI systems
                          • 1. Track usage, performance, and errors
                            • 2. Implement logging and alerting
                              - Evaluate and test generative AI applications
                              • 1. Define evaluation metrics and criteria
                                • 2. Test for safety, accuracy, and relevance
                                  Design and implement a GenAIOps infrastructure20–25%- Set up Microsoft Foundry environment
                                  • 1. Configure projects, connections, and security
                                    • 2. Manage compute and deployment resources
                                      - Implement infrastructure for generative AI workloads
                                      • 1. Integrate with Azure services and tools
                                        • 2. Design scalable and secure architecture
                                          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. Manage compute targets, datastores, and environments
                                                • 2. Configure workspace settings and security

                                                  Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:

                                                  You manage an Azure Machine Learning workspace. You have an environment for training jobs which uses an existing Docker image.
                                                  A new version of the Docker image is available.
                                                  You need to use the latest version of the Docker image for the environment configuration by using the Azure Machine Learning SDK v2.
                                                  What should you do?

                                                  • A. Use the Environment class to create a new version of the environment.
                                                  • B. Use the create_or_update method to change the tag of the image.
                                                  • C. Change the description parameter of the environment configuration.
                                                  • D. Modify the conda_file to specify the new version of the Docker image.
                                                  Reveal Solution  Discussion  0

                                                  Correct Answer: A  🗳️

                                                  Explanation: Only visible for Pass4sures members. You can sign-up / login (it's free).

                                                  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 a list of numerical metrics.
                                                  You need to implement a method to log a list of numerical metrics.
                                                  Which method should you use?

                                                  • A. mlflow.log_image()
                                                  • B. mlflow.log_artifact()
                                                  • C. mlflow.log_batch()
                                                  • D. mlflow.log_metric()
                                                  Reveal Solution  Discussion  0

                                                  Correct Answer: C  🗳️

                                                  Explanation: Only visible for Pass4sures members. You can sign-up / login (it's free).

                                                  You have an Azure Machine Learning workspace that includes an AmICompute cluster and a batch endpoint.
                                                  You clone a repository that contains an MLflow model to your local computer. You need to ensure that you can deploy the model to the batch endpoint.
                                                  Solution: Create a data asset in the workspace.
                                                  Does the solution meet the goal?

                                                  • A. No
                                                  • B. Yes
                                                  Reveal Solution  Discussion  0

                                                  Correct Answer: A  🗳️

                                                  You need to recommend an experiment-tracking strategy that ensures consistent experiment results.
                                                  What should you recommend?

                                                  • A. Application Insights logs
                                                  • B. Azure Monitor alerts
                                                  • C. MLflow experiment tracking
                                                  • D. Azure Machine Learning job output logs
                                                  Reveal Solution  Discussion  0

                                                  Correct Answer: C  🗳️

                                                  Explanation: Only visible for Pass4sures members. You can sign-up / login (it's free).

                                                  You train and register an Azure Machine Learning model
                                                  You plan to deploy the model to an online endpoint
                                                  You need to ensure that applications will be able to use the authentication method with a non-expiring artifact to access the model.
                                                  Solution:
                                                  Create a managed online endpoint with the default authentication settings. Deploy the model to the online endpoint.
                                                  Does the solution meet the goal?

                                                  • A. No
                                                  • B. Yes
                                                  Reveal Solution  Discussion  0

                                                  Correct Answer: A  🗳️

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