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Microsoft AI-300 Exam Syllabus Topics:

SectionWeightObjectives
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
          Implement machine learning model lifecycle and operations25–30%- Orchestrate model training and experimentation
          • 1. Track experiments and metrics
            • 2. Create and manage pipelines
              - Monitor and maintain models in production
              • 1. Implement retraining and update workflows
                • 2. Monitor data and model drift
                  - Deploy models to production
                  • 1. Configure deployment options and scaling
                    • 2. Deploy to real-time and batch endpoints
                      - Register, version, and package models
                      • 1. Manage model registry
                        • 2. Create reusable model packages
                          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
                                          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. Manage resource utilization
                                                • 2. Optimize inference and deployment

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                                                  Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions (Q113-Q118):

                                                  NEW QUESTION # 113
                                                  Hotspot Question
                                                  You manage an Azure Machine Learning workspace named workspace1.
                                                  You must register an Azure Blob storage datastore in workspace1 by using an access key. You develop Python SDK v2 code to import all modules required to register the datastore.
                                                  You need to complete the Python SDK v2 code to define the datastore.
                                                  How should you complete the code? To answer, select the appropriate options in the answer area.
                                                  NOTE: Each correct selection is worth one point.

                                                  Answer:

                                                  Explanation:

                                                  Explanation:
                                                  How should you complete the code?
                                                  Box 1: container_name
                                                  container_name specifies the parameter name used in the AzureBlobDatastore constructor to identify your target blob storage container.
                                                  Box 2: wasbs
                                                  Correct Code Formats
                                                  Depending on your preference for the storage connection protocol, your completed line of code should look like one of the following variations:
                                                  Using the standard HTTPS protocol (Default).
                                                  -> Using the WASBS (Windows Azure Storage Blob Secure).
                                                  Reference:
                                                  https://stackoverflow.com/questions/75275875/create-an-azureblobdatastore-with-sdk-v2


                                                  NEW QUESTION # 114
                                                  Drag and Drop Question
                                                  A team manages prompts that are used by a generative AI application built on Microsoft Foundry.
                                                  Multiple developers contribute prompt updates, and changes must be reviewed and tracked over time.
                                                  The team requires that:
                                                  - Prompt changes are reviewed before being applied to the version in
                                                  production.
                                                  - Previous prompt versions can be restored if issues occur.
                                                  - Prompt updates follow the same governance practices as the
                                                  application code.
                                                  You need to implement a controlled process for managing and updating prompts in production.
                                                  How should you manage prompt updates to meet the requirements? To answer, move the appropriate actions to the correct requirements. You may use each action once, more than once, or not at all. You may need to move the split bar between panes or scroll to view content.
                                                  NOTE: Each correct selection is worth one point.

                                                  Answer:

                                                  Explanation:


                                                  NEW QUESTION # 115
                                                  Fabrikam Inc. must improve its deployment process because traditional machine learning models are deployed manually and the organization has limited rollback capability .
                                                  You need to recommend a deployment approach that supports staged rollout and rollback while minimizing operational overhead.
                                                  Which deployment approach should you recommend?

                                                  Answer: D

                                                  Explanation:
                                                  Managed online endpoints with traffic splitting are the best fit because Azure Machine Learning supports multiple deployments behind a single online endpoint and allows production traffic to be distributed between them. This enables a blue-green or progressive rollout strategy : deploy the new model version alongside the current version, validate it independently, send a small percentage of production traffic to it, monitor behavior, and then gradually increase traffic. If problems occur, traffic can be redirected immediately to the previous deployment, providing a practical rollback mechanism.
                                                  This approach also aligns with Fabrikam's requirement to favor managed services and automation because the team has limited Azure DevOps experience. Managed online endpoints handle much of the serving, scaling, security, and monitoring infrastructure automatically and support both traffic splitting and mirrored traffic for safer validation.
                                                  Option B can also implement blue-green deployment, but AKS introduces substantially more infrastructure and operational management than Fabrikam requires. Option A would require custom deployment, routing, scaling, and rollback logic. Option D is intended for asynchronous batch inference rather than staged real-time production serving.
                                                  Study Guide Reference: Implement machine learning model lifecycle and operations - managed online endpoints, multiple deployments, traffic splitting, blue-green deployment, staged rollout, monitoring, and rollback.


                                                  NEW QUESTION # 116
                                                  A real-time endpoint is deployed in Azure Machine Learning to serve predictions to a web application.
                                                  Users report intermittent failures and unexpected responses when calling the endpoint.
                                                  You need to identify the appropriate troubleshooting action for each reported issue.
                                                  Which troubleshooting action should you perform for each issue? To answer, move the appropriate troubleshooting actions to the correct issues. You may use each troubleshooting action once, more than once, or not at all. You may need to move the split bar between panes or scroll to view content . NOTE: Each correct selection is worth one point.

                                                  Answer:

                                                  Explanation:

                                                  Explanation:
                                                  For deployment failures when containers do not start, review deployment logs in Azure ML Studio or use the CLI get-logs command to surface dependency installation errors, missing files, or scoring script import failures. For authentication errors such as 401 or 403 responses, check that client applications are using valid authentication keys or bearer tokens, as mismatched or expired credentials cause auth failures. For performance degradation and slow responses, review compute instance scaling configuration, since a surge in traffic saturates a fixed number of replicas causing queuing and high latency without autoscaling. For internal server errors such as HTTP 500, these typically originate in the scoring script, so inspect the init and run functions for unhandled exceptions and verify the environment has all required packages installed.
                                                  Microsoft Learn Reference Topic: Troubleshoot online endpoint deployment failures - Azure Machine Learning


                                                  NEW QUESTION # 117
                                                  You manage an Azure Machine Learning workspace. You use Azure Machine Learning Python SDK v2 to configure a trigger to schedule a pipeline job. You need to create a time-based schedule with recurrence pattern.
                                                  Which two properties must you use to successfully configure the trigger? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.

                                                  Answer: B,C


                                                  NEW QUESTION # 118
                                                  ......

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