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Microsoft AI-300 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: 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. Monitor data and model drift
        • 2. Implement retraining and update workflows
          - Deploy models to production
          • 1. Configure deployment options and scaling
            • 2. Deploy to real-time and batch endpoints
              - Register, version, and package models
              • 1. Create reusable model packages
                • 2. Manage model registry
                  Topic 2: 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. Tune prompts and generation settings
                        • 2. Choose appropriate models and parameters
                          Topic 3: Design and implement an MLOps infrastructure15–20%- Create and manage Machine Learning workspace resources and assets
                          • 1. Configure workspace settings and security
                            • 2. Manage compute targets, datastores, and environments
                              - Implement infrastructure as code for Machine Learning
                              • 1. Use Bicep or Azure CLI to deploy resources
                                • 2. Automate infrastructure provisioning
                                  Topic 4: 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 5: 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. Design scalable and secure architecture
                                                • 2. Integrate with Azure services and tools

                                                  Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:

                                                  1. A real-time endpoint experiences sporadic latency spikes. Investigation reveals instances scale down to zero during inactivity. You need to reduce latency without significantly increasing cost.
                                                  What should you configure?

                                                  A) Increase timeout settings
                                                  B) Reduce logging
                                                  C) Switch to batch endpoint
                                                  D) Set minimum instance count


                                                  2. You have a deployment of an Azure OpenAI Service base model.
                                                  You plan to fine-tune the model.
                                                  You need to prepare a file that contains training data for multi-turn chat.
                                                  Which file encoding method should you use?

                                                  A) ISO-8859-1
                                                  B) UTF-16
                                                  C) UTF-8
                                                  D) ASCII


                                                  3. An organization maintains separate Azure Machine Learning workspaces for development and production.
                                                  Both environments must use the same validated assets without duplicating them.
                                                  Assets must be shared across workspaces while maintaining centralized governance and version control.
                                                  You need to enable reuse of assets across workspaces without copying them.
                                                  What should you do?

                                                  A) Enable workspace-level Git integration and sync assets between repositories.
                                                  B) Create a shared Azure Machine Learning environment that includes the asset.
                                                  C) Publish the asset as a pipeline component.
                                                  D) Publish the asset to an Azure Machine Learning registry.


                                                  4. You manage a Microsoft Foundry project. You build a multi-turn chatbot application.
                                                  You plan to filter your traces to identify issues while observing how the application is responding.
                                                  The solution must not use an external knowledge base.
                                                  You need to select an evaluation metric.
                                                  Which built-in evaluator should you use?

                                                  A) RelevanceEvaluator
                                                  B) SimilarityEvaluator
                                                  C) QAEvaluator
                                                  D) CoherenceEvaluator


                                                  5. Drag and Drop Question
                                                  A team is deploying a new version of a customer scoring model to a production online endpoint in Azure Machine Learning.
                                                  The team must minimize risk by gradually introducing the new model version and ensuring that traffic can be reverted immediately if issues occur.
                                                  You need to roll out the new model according to the requirements.
                                                  Which four actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.


                                                  Solutions:

                                                  Question # 1
                                                  Answer: D
                                                  Question # 2
                                                  Answer: C
                                                  Question # 3
                                                  Answer: D
                                                  Question # 4
                                                  Answer: D
                                                  Question # 5
                                                  Answer: Only visible for members

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