素晴らしいAI-300受験記 &資格試験のリーダー &最高のAI-300試験

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Microsoft AI-300 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: 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. Configure projects, connections, and security
        • 2. Manage compute and deployment resources
          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. Tune prompts and generation settings
                • 2. Choose appropriate models and parameters
                  Topic 3: Design and implement an MLOps infrastructure15–20%- Create and manage Machine Learning workspace resources and assets
                  • 1. Manage compute targets, datastores, and environments
                    • 2. Configure workspace settings and security
                      - Implement infrastructure as code for Machine Learning
                      • 1. Use Bicep or Azure CLI to deploy resources
                        • 2. Automate infrastructure provisioning
                          Topic 4: Implement machine learning model lifecycle and operations25–30%- 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
                                  - Orchestrate model training and experimentation
                                  • 1. Track experiments and metrics
                                    • 2. Create and manage pipelines
                                      - Deploy models to production
                                      • 1. Deploy to real-time and batch endpoints
                                        • 2. Configure deployment options and scaling
                                          Topic 5: 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

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                                                  Microsoft Operationalizing Machine Learning and Generative AI Solutions 認定 AI-300 試験問題 (Q35-Q40):

                                                  質問 # 35
                                                  You create an Azure Machine Learning workspace and install the MLflow library.
                                                  You need to log different types of data by using the MLflow library.
                                                  Which method should you use? To answer, select the appropriate options in the answer area.
                                                  NOTE: Each correct selection is worth one point.

                                                  正解：

                                                  解説：

                                                  Explanation:


                                                  質問 # 36
                                                  You manage an Azure Machine Learning workspace. You submit a training job with the Azure Machine Learning Python SDK v2. You must use MLflow to log metrics, model parameters, and model artifacts automatically when training a model.
                                                  You start by writing the following code segment:

                                                  For each of the following statements, select Yes If the statement is true. Otherwise, select No.

                                                  正解：

                                                  解説：

                                                  Explanation:


                                                  質問 # 37
                                                  You create a multi-class image classification deep learning model.
                                                  The model must be retrained monthly with the new image data fetched from a public web portal. You create an Azure Machine Learning pipeline to fetch new data, standardize the size of images and retrain the model.
                                                  You need to use the Azure Machine Learning Python SEX v2 to configure the schedule for the pipeline. The schedule should be defined by using the frequency and interval properties with frequency set to month ' and interval set to " 1:
                                                  Which three classes should you instantiate in sequence " ' To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

                                                  正解：

                                                  解説：

                                                  Explanation:


                                                  質問 # 38
                                                  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. Some question sets might have more than one correct solution, while others might not have a correct solution.
                                                  After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear on the review screen.
                                                  You work in Microsoft Foundry with a prompt flow.
                                                  You must manually evaluate prompts and compare results across prompt variants.
                                                  You need to capture the inputs, outputs, token usage, and latencies for each flow run for the evaluation.
                                                  Solution: In Microsoft Foundry, turn on Tracing for the prompt flow of the project and execute test runs to produce trace data.
                                                  Does the solution meet the goal?

                                                  正解：A

                                                  解説：
                                                  Navigate to the Microsoft Foundry project in the Azure portal or Foundry portal and open the prompt flow for which you want to capture telemetry. Locate the Tracing toggle in the project or flow settings and enable it - this single action instructs the Foundry runtime to instrument every LLM call within the flow. Execute test runs of the prompt flow through the portal ' s test interface or via the CLI. Open the Traces view in Microsoft Foundry: each run appears as a trace with a tree of spans, where each span corresponds to one LLM call or tool invocation. Every span captures the exact input sent to the model, the model ' s output, token counts including prompt tokens and completion tokens, and wall-clock latency. Compare runs across prompt variants by examining individual traces side-by-side. This solution directly satisfies all four capture requirements.
                                                  Microsoft Learn Reference Topic: Enable and use tracing in Microsoft Foundry - Capturing LLM call telemetry for evaluation


                                                  質問 # 39
                                                  You have an Azure Machine Learning workspace.
                                                  You plan to run a job to tram a model as an MLflow model output.
                                                  You need to specify the output mode of the MLflow model.
                                                  Which three modes can you specify? Each correct answer presents a complete solution.
                                                  NOTE: Each correct selection is worth one point.

                                                  正解：A、B、D


                                                  質問 # 40
                                                  ......

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                                                  AI-300試験: https://www.mogiexam.com/AI-300-exam.html