AI-300ソフトウエア & AI-300日本語練習問題

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Microsoft AI-300 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: 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 2: Optimize generative AI systems and model performance15–20%- Improve efficiency and cost-effectiveness
          • 1. Manage resource utilization
            • 2. Optimize inference and deployment
              - Optimize model selection and configuration
              • 1. Choose appropriate models and parameters
                • 2. Tune prompts and generation settings
                  Topic 3: Implement machine learning model lifecycle and operations25–30%- 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
                          - 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 4: Implement generative AI quality assurance and observability10–15%- Evaluate and test generative AI applications
                                  • 1. Test for safety, accuracy, and relevance
                                    • 2. Define evaluation metrics and criteria
                                      - Monitor generative AI systems
                                      • 1. Track usage, performance, and errors
                                        • 2. Implement logging and alerting
                                          Topic 5: Design and implement a GenAIOps infrastructure20–25%- Implement infrastructure for generative AI workloads
                                          • 1. Integrate with Azure services and tools
                                            • 2. Design scalable and secure architecture
                                              - Set up Microsoft Foundry environment
                                              • 1. Manage compute and deployment resources
                                                • 2. Configure projects, connections, and security

                                                  >> AI-300ソフトウエア <<

                                                  正確的AI-300｜最高のAI-300ソフトウエア試験｜試験の準備方法Operationalizing Machine Learning and Generative AI Solutions日本語練習問題

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                                                  Microsoft Operationalizing Machine Learning and Generative AI Solutions 認定 AI-300 試験問題 (Q116-Q121):

                                                  質問 # 116
                                                  -
                                                  You have an Azure Machine Learning workspace and a collection of image files stored in two Azure Blob Storage accounts.
                                                  You need to configure data asset properties.
                                                  Which values should you use in your configuration? To answer, select the appropriate options in the answer area.
                                                  NOTE: Each correct selection is worth one point.

                                                  正解：

                                                  解説：

                                                  Explanation:
                                                  Data asset type: mltable
                                                  Scheme of the URL paths to storage locations: wasbs
                                                  The correct data asset type is mltable because the image collection is distributed across multiple storage locations . Microsoft identifies MLTable as the appropriate Azure Machine Learning data asset type when unstructured data such as images, text, or audio is spread across multiple storage locations. An MLTable definition can contain multiple file, folder, or pattern entries, allowing one logical data asset to reference image data residing in different storage accounts.
                                                  A uri_folder data asset is appropriate when the required unstructured files reside within a single folder path .
                                                  It represents one folder location and therefore does not provide the same multi-path abstraction required by this scenario. uri_file represents only an individual file and is clearly unsuitable for a collection of images.
                                                  For the path scheme, Azure Machine Learning uses wasbs:// for Azure Blob Storage. Microsoft documents the canonical format as:
                                                  wasbs:// < container > @ < account > .blob.core.windows.net/ < path >
                                                  By comparison, abfss:// is used for Azure Data Lake Storage Gen2, while azureml:// identifies Azure Machine Learning datastore paths rather than direct Azure Blob Storage URLs.
                                                  Therefore, an MLTable containing multiple wasbs:// paths correctly represents image files distributed across the two Azure Blob Storage accounts.
                                                  Study Guide Reference: Design and implement an MLOps infrastructure - Azure Machine Learning data assets, MLTable, multi-location datasets, storage URI schemes, and Blob Storage integration.


                                                  質問 # 117
                                                  Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals.
                                                  You have an Azure Machine Learning workspace. You connect to a terminal session from the Notebooks page in Azure Machine Learning studio.
                                                  You plan to add a new Jupyter kernel that will be accessible from the same terminal session.
                                                  You need to perform the task that must be completed before you can add the new kernel.
                                                  Solution: Create an environment.
                                                  Does the solution meet the goal?

                                                  正解：A

                                                  解説：
                                                  Yes. Before registering a new Jupyter kernel on an Azure Machine Learning compute instance, you create a separate environment that will contain the Python runtime and packages associated with that kernel.
                                                  Microsoft's current Azure Machine Learning terminal guidance explicitly starts the new-kernel procedure by creating a Conda environment, for example:
                                                  conda create --name newenv
                                                  The environment is then activated by using conda activate newenv. After activation, pip and ipykernel are installed into that environment, and the environment is finally registered as a Jupyter kernel by using python - m ipykernel install.
                                                  This separation is important because each Jupyter kernel should execute against a defined Python environment containing its own dependencies. It prevents package changes for one notebook workload from unexpectedly affecting another kernel or the Azure Machine Learning system environment.
                                                  Therefore, creating an environment is a valid prerequisite in the process of adding a new Jupyter kernel from the Azure Machine Learning terminal. The environment provides the runtime context that is subsequently exposed to Jupyter through its kernel specification.
                                                  Study Guide Reference: Design and implement an MLOps infrastructure - Azure Machine Learning compute instances, Jupyter kernels, Conda environments, dependency isolation, and interactive development.


                                                  質問 # 118
                                                  You have an Azure Machine Learning (ML) model deployed to an online endpoint.
                                                  You need to review container logs from the endpoint by using Azure Ml Python SDK v2. The logs must include the console log from the inference server with print/log statements from the models scoring script.
                                                  What should you do first?

                                                  正解：D


                                                  質問 # 119
                                                  You manage an Azure Machine learning workspace.
                                                  You build a custom model you must log with Mlftow. The custom model includes the following:
                                                  * The model is not natively supported by Mlflow.
                                                  * The model cannot be serialized in Pickle format.
                                                  * The model source code is complex.
                                                  * The Python library tor the model must be packaged with the model.
                                                  You need to create a custom model flavor to enable logging with ML. flow.
                                                  What should you use?

                                                  正解：A


                                                  質問 # 120
                                                  A pipeline step fails intermittently due to transient compute issues. You need to improve reliability without modifying core logic or increasing cost significantly. What is the BEST approach?

                                                  正解：D

                                                  解説：
                                                  Retry policies allow pipeline steps to automatically recover from transient failures, such as temporary compute or network issues. This improves reliability without modifying core logic or increasing infrastructure costs. Increasing compute resources does not address transient failure scenarios effectively.


                                                  質問 # 121
                                                  ......

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