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Microsoft AI-300 Exam Syllabus Topics:

SectionWeightObjectives
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%- 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. Automate infrastructure provisioning
                • 2. Use Bicep or Azure CLI to deploy resources
                  Implement machine learning model lifecycle and operations25–30%- 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
                          - Monitor and maintain models in production
                          • 1. Implement retraining and update workflows
                            • 2. Monitor data and model drift
                              - Orchestrate model training and experimentation
                              • 1. Track experiments and metrics
                                • 2. Create and manage pipelines
                                  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. Manage compute and deployment resources
                                        • 2. Configure projects, connections, and security
                                          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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                                                  Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions (Q84-Q89):

                                                  NEW QUESTION # 84
                                                  You create an MLflow model
                                                  You must deploy the model to Azure Machine Learning for batch inference.
                                                  You need to create the batch deployment.
                                                  Which two components should you use? Each correct answer presents a complete solution.
                                                  NOTE: Each correct selection is worth one point

                                                  Answer: B,D


                                                  NEW QUESTION # 85
                                                  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?

                                                  Answer: A


                                                  NEW QUESTION # 86
                                                  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?

                                                  Answer: D


                                                  NEW QUESTION # 87
                                                  Drag and Drop Question
                                                  A team validates a generative AI application that produces free-form text responses by using Microsoft Foundry SDK.
                                                  The evaluation dataset is registered in the Microsoft Foundry environment.
                                                  You need to configure a safety evaluation pipeline that reliably evaluates model outputs for harmful content.
                                                  Which three 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.

                                                  Answer:

                                                  Explanation:


                                                  NEW QUESTION # 88
                                                  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?

                                                  Answer: C

                                                  Explanation:
                                                  For preparing a multi-turn training data file for the Azure OpenAI Service, you should use UTF-8 with a Byte Order Mark (BOM) encoding.
                                                  File Format Requirements
                                                  Format: The file must be in JSON Lines (JSONL) format, where each individual line is a valid JSON object representing one training example.
                                                  Encoding: Specifically, Azure OpenAI requires the JSONL file to be encoded in UTF-8 with BOM.
                                                  Structure: For multi-turn conversations, each line must contain a messages array with multiple role ("system", "user", "assistant") and content pairs to represent the dialogue history.
                                                  Reference:
                                                  https://dev.to/icebeam7/fine-tuning-a-model-with-azure-open-ai-studio-39p7


                                                  NEW QUESTION # 89
                                                  ......

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