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Microsoft AI-300 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: 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
      - 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
              - Register, version, and package models
              • 1. Manage model registry
                • 2. Create reusable model packages
                  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: 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. Configure workspace settings and security
                                • 2. Manage compute targets, datastores, and environments
                                  Topic 4: 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
                                          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

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                                                  Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions (Q67-Q72):

                                                  NEW QUESTION # 67
                                                  You create a binary classification model. You use the Fairlearn package to assess model fairness.
                                                  You must eliminate the need to retrain the model.
                                                  You need to implement the Fairlearn package.
                                                  Which algorithm should you use?

                                                  Answer: D


                                                  NEW QUESTION # 68
                                                  A data science team completes multiple training runs within an experiment by using MLflow.
                                                  The team wants to store a selected model in Azure Machine Learning so that it can be versioned and deployed later.
                                                  The model must be versioned centrally for reuse across environments.
                                                  You need to version the trained model.
                                                  Which two actions should you perform? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point. Choose two .

                                                  Answer: B,C

                                                  Explanation:
                                                  MLflow training runs produce model artifacts - the serialized model files, conda environment, and MLmodel specification - stored in the run ' s outputs folder. These artifacts are transient run outputs but are not yet a versioned, named model that can be deployed. To make the model a first-class, versioned, deployable artifact, you must explicitly register it. Locating artifacts from the run (action A) is necessary because you need the run ' s artifact URI, typically in the form runs:/run_id/model, to register from.
                                                  Registering in the AML workspace (action B) creates an entry in the model registry with a name and auto- incremented version, making the model discoverable, governable, and deployable across environments.
                                                  Tagging the experiment (option C) does not version the model. Exporting to local storage (option D) removes the model from Azure ML ' s managed infrastructure, losing lineage and governance.
                                                  Microsoft Learn Reference Topic: Register MLflow models in the Azure Machine Learning model registry


                                                  NEW QUESTION # 69
                                                  A team is working in Microsoft Foundry to test and compare large language model (LLM) prompt variants in a development environment.
                                                  The team requires consistent inputs to evaluate prompt variants without relying on live user traffic.
                                                  You need to create a controlled evaluation of input data.
                                                  Which action should you perform first?

                                                  Answer: B

                                                  Explanation:
                                                  The team cannot rely on live user traffic for evaluation because it would make results non-reproducible and could expose users to untested prompts. Synthetic data generation within Microsoft Foundry allows the team to produce diverse, representative input examples that simulate real user queries without any live traffic risk, giving a stable, consistent test set evaluated under identical conditions. Option B (content filters) and Option C (blocklists) are safety controls applied at inference time, not evaluation inputs. Option D (observability metrics) is a monitoring capability for production systems. With synthetic data, the team can construct edge cases, varied phrasings, and domain-specific scenarios that would take months to accumulate organically from real users, directly enabling the controlled evaluation the question requires without any dependency on live traffic.
                                                  Microsoft Learn Reference Topic: Evaluate generative AI apps with synthetic data - Microsoft Foundry prompt evaluation


                                                  NEW QUESTION # 70
                                                  Hotspot Question
                                                  You review the following Azure CLI command and the relevant Bicep excerpt.

                                                  (Non-relevant sections are omitted.)
                                                  You need to validate what the snippet will do before it is merged. For each of the following statements, select Yes if the statement is true. Otherwise, select No.
                                                  NOTE: Each correct selection is worth one point.

                                                  Answer:

                                                  Explanation:


                                                  NEW QUESTION # 71
                                                  You create an Azure Machine Learning workspace
                                                  You are developing a Python SDK v2 notebook to perform custom model training in the workspace. The notebook code imports all required packages.
                                                  You need to complete the Python SDK v2 code to include a training script. environment, and compute information.
                                                  How should you complete ten code? To answer, select the appropriate options in the answer area.
                                                  NOTE: Each correct selection is worth one point

                                                  Answer:

                                                  Explanation:

                                                  Explanation:


                                                  NEW QUESTION # 72
                                                  ......

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