有難いMicrosoft AI-300｜100％合格率のAI-300全真模擬試験試験｜試験の準備方法Operationalizing Machine Learning and Generative AI Solutionsファンデーション

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Microsoft AI-300 Exam Syllabus Topics:

SectionWeightObjectives
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
          - Register, version, and package models
          • 1. Manage model registry
            • 2. Create reusable model packages
              - Deploy models to production
              • 1. Configure deployment options and scaling
                • 2. Deploy to real-time and batch endpoints
                  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
                          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
                                  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. Track usage, performance, and errors
                                        • 2. Implement logging and alerting
                                          Optimize generative AI systems and model performance15–20%- 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

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                                                  Microsoft Operationalizing Machine Learning and Generative AI Solutions 認定 AI-300 試験問題 (Q184-Q189):

                                                  質問 # 184
                                                  Drag and Drop Question
                                                  A company plans to deploy a foundation model in Microsoft Foundry.
                                                  The mode must support the following workloads:
                                                  - A customer support workload used across multiple regions
                                                  - A marketing workload that must remain within a specific region due to data residency requirements You need to select the deployment type.
                                                  Which deployment type should you use for each workload? To answer, move the appropriate deployment types to the correct requirements. You may use each deployment type 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.

                                                  正解：

                                                  解説：


                                                  質問 # 185
                                                  A team runs training and inference jobs in Azure Machine Learning.
                                                  The team experiences inconsistent runtime dependencies that cause variation in results.
                                                  You need to ensure that all jobs use the same execution dependencies.
                                                  Which asset should you define?

                                                  正解：A

                                                  解説：
                                                  To guarantee consistent runtime dependencies in Azure Machine Learning, you must use Azure ML Environments configured with custom Docker images or pinned Conda dependencies.
                                                  An Environment asset encapsulates the exact Python packages, environment variables, and software settings for your training and inference workloads.
                                                  Reference:
                                                  https://learn.microsoft.com/en-us/azure/machine-learning/how-to-set-up-training-targets


                                                  質問 # 186
                                                  You manage an Azure Machine Learning workspace named workspace1 by using the Python SDK v2.
                                                  You must register datastores in workspace1 for Azure Blob and Azure Data Lake Gen2 storage to meet the following requirements:
                                                  * Data scientists accessing the datastore must have the same level of access.
                                                  * Access must be restricted to specified containers or folders.
                                                  You need to configure a security access method used to register the Azure Blob and Azure Data lake Gen?
                                                  storage in workspace1. Which security access method should you configure? To answer, select the appropriate options in the answers area.
                                                  NOTE: Each correct selection is worth one point.

                                                  正解：

                                                  解説：

                                                  Explanation:


                                                  質問 # 187
                                                  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.

                                                  正解：A、E


                                                  質問 # 188
                                                  You have a Microsoft Foundry project.
                                                  You plan to use the Microsoft Foundry portal to fine-tune a base Azure OpenAI Service model that can accept both text and images as input.
                                                  You need to choose the suitable model.
                                                  Which model should you choose?

                                                  正解：A

                                                  解説：
                                                  GPT-4o is OpenAI ' s multimodal model that accepts both text and image inputs natively. The ' o ' in GPT-4o stands for ' omni, ' reflecting its ability to process multiple modalities in a single inference call. It is available in Microsoft Foundry for both inference and fine-tuning, and it supports vision inputs alongside text prompts, making it the correct choice for any fine-tuning scenario requiring multimodal input. Davinci-002 (option A) is a legacy text-completion model with no vision capabilities and limited fine-tuning support for modern chat formats. GPT-3.5-Turbo (option C) is a text-only chat model that does not accept image inputs. GPT-4 (option D) has a standard version that is text-only; the vision variant GPT-4V is distinct from GPT-4o and has more limited fine-tuning availability in Foundry. For multimodal text plus image fine-tuning, GPT-4o is the only correct choice.
                                                  Microsoft Learn Reference Topic: Azure OpenAI Service models available for fine-tuning - GPT-4o multimodal capabilities


                                                  質問 # 189
                                                  ......

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