最新版的AI-300測試題庫,覆蓋全真Operationalizing Machine Learning and Generative AI Solutions AI-300考試考題

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Microsoft AI-300 Exam Syllabus Topics:

Section Weight Objectives
Implement generative AI quality assurance and observability 10–15% - Monitor generative AI systems
  • 1. Implement logging and alerting
    • 2. Track usage, performance, and errors
      - Evaluate and test generative AI applications
      • 1. Define evaluation metrics and criteria
        • 2. Test for safety, accuracy, and relevance
          Optimize generative AI systems and model performance 15–20% - Optimize model selection and configuration
          • 1. Choose appropriate models and parameters
            • 2. Tune prompts and generation settings
              - Improve efficiency and cost-effectiveness
              • 1. Manage resource utilization
                • 2. Optimize inference and deployment
                  Design and implement a GenAIOps infrastructure 20–25% - Set up Microsoft Foundry environment
                  • 1. Manage compute and deployment resources
                    • 2. Configure projects, connections, and security
                      - Implement infrastructure for generative AI workloads
                      • 1. Integrate with Azure services and tools
                        • 2. Design scalable and secure architecture
                          Implement machine learning model lifecycle and operations 25–30% - 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
                                  - Register, version, and package models
                                  • 1. Create reusable model packages
                                    • 2. Manage model registry
                                      - Deploy models to production
                                      • 1. Configure deployment options and scaling
                                        • 2. Deploy to real-time and batch endpoints
                                          Design and implement an MLOps infrastructure 15–20% - 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

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                                                  最新的 Microsoft Certified AI-300 免費考試真題 (Q44-Q49):

                                                  問題 #44
                                                  You manage an Azure Machine Learning workspace.
                                                  An MLflow model is already registered. You plan to customize how the deployment does inference. You need to deploy the MLflow model to a batch endpoint for batch inferencing. What should you create first?

                                                  • A. environment
                                                  • B. scoring script
                                                  • C. deployment
                                                  • D. deployment definition

                                                  答案:B


                                                  問題 #45
                                                  Drag and Drop Question
                                                  You manage an Azure Machine Learning workspace. You train a model named model1.
                                                  You must identify the features to modify for a differing model prediction result.
                                                  You need to configure the Responsible AI (RAI) dashboard for model1.
                                                  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.

                                                  答案:

                                                  解題說明:

                                                  Explanation:
                                                  To setup the Responsible AI (RAI) dashboard in Azure Machine Learning and specifically analyze minimal feature modifications needed to change a prediction result, you must use Counterfactual Analysis.
                                                  Here are the specific sequential steps you need to take:
                                                  Step 1: Load and configure the Responsible AI Insights dashboard constructor component.
                                                  Initialize the RAI Insights Dashboard Constructor
                                                  1. Create the root pipeline component that acts as the container for your tools.
                                                  2. Call the RAI Insights dashboard constructor component.
                                                  3. Pass your registered model and your test dataset as the mandatory inputs to this component.
                                                  Step 2: Add the Counterfactuals to Responsible AI Insights dashboard
                                                  Configure Counterfactual Analysis
                                                  1. Add the specific component required to identify what features to modify for a differing prediction.
                                                  2. Call the Add Counterfactuals to RAI Insights dashboard component.
                                                  3. Link its input to the output of the constructor component initialized in Step 1 above.
                                                  4. Configure the parameter variables, including the number of counterfactual examples you want to generate per data point.
                                                  Step 3: Use the Gather Responsible AI Insights dashboard component to present the dashboard.
                                                  Assemble and Submit the Dashboard Pipeline
                                                  1. Gather the components into a cohesive Azure ML pipeline job to execute them.
                                                  -> 2. Call the Gather RAI Insights dashboard component to aggregate the constructor and the counterfactual tool outputs.
                                                  3. Submit the pipeline job to your Azure Machine Learning workspace compute cluster for processing.
                                                  Reference:
                                                  https://oneuptime.com/blog/post/2026-02-16-how-to-implement-responsible-ai-dashboards-in-azure-machine-learning/view


                                                  問題 #46
                                                  You deploy a new model version to a managed online endpoint. You must test it with 10% traffic and automatically roll back if latency or error rate increases beyond threshold. What should you configure?

                                                  • A. Manual testing workflow
                                                  • B. Batch endpoint validation
                                                  • C. Traffic splitting with monitoring alerts
                                                  • D. Separate endpoint for testing

                                                  答案:C


                                                  問題 #47
                                                  You create an Azure Machine Learning workspace named woricspace1. The workspace contains a Python SDK v2 notebook that uses MLflow to collect model training metrics and artifacts from your local computer.
                                                  You must reuse the notebook to run on Azure Machine Learning compute instance in workspace1.
                                                  You need to continue to log metrics and artifacts from your data science code.
                                                  What should you do?

                                                  • A. Configure the tracking URI.
                                                  • B. Log into workspace " !.
                                                  • C. Instantiate the job class.
                                                  • D. Instantiate the MLCIient class.

                                                  答案:A


                                                  問題 #48
                                                  You have an Azure subscription named Sub1 that contains an Azure Machine Learning workspace named Workspace1. Workspace1 contains the following assets:
                                                  - a registered MLflow model named Model1
                                                  - an online endpoint named Endpoint1
                                                  Outbound network connectivity from Endpoint1 is blocked.
                                                  You need to deploy Model1 to Endpoint1.
                                                  What should you do first?

                                                  • A. In Sub1, create an Azure Machine Learning registry.
                                                  • B. In Sub1, create a private endpoint.
                                                  • C. In Workspace1, create a package.
                                                  • D. In Workspace1, create a linked service.

                                                  答案:C

                                                  解題說明:
                                                  To successfully deploy the registered MLflow model to an online endpoint that lacks outbound internet connectivity, you must use model packaging to build a deployment package before deploying.
                                                  By default, Azure Machine Learning deploys MLflow models using a "no-code deployment" approach, which requires an outbound internet connection during container runtime to dynamically download and install Python dependencies listed in the model's conda.yaml file.
                                                  Because outbound connectivity is blocked, this process fails. Packaging the model bundles the model binaries, dependencies, and environment ahead of time, entirely removing the requirement for runtime internet access.
                                                  Reference:
                                                  https://docs.azure.cn/en-us/machine-learning/how-to-deploy-mlflow-models-online-endpoints


                                                  問題 #49
                                                  ......

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