AI-300인증시험덤프공부문제

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Microsoft AI-300 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: 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. Optimize inference and deployment
        • 2. Manage resource utilization
          Topic 2: 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. Design scalable and secure architecture
                • 2. Integrate with Azure services and tools
                  Topic 3: Implement generative AI quality assurance and observability10–15%- Monitor generative AI systems
                  • 1. Implement logging and alerting
                    • 2. Track usage, performance, and errors
                      - Evaluate and test generative AI applications
                      • 1. Test for safety, accuracy, and relevance
                        • 2. Define evaluation metrics and criteria
                          Topic 4: 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 5: Implement machine learning model lifecycle and operations25–30%- 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
                                          - Monitor and maintain models in production
                                          • 1. Monitor data and model drift
                                            • 2. Implement retraining and update workflows
                                              - Deploy models to production
                                              • 1. Deploy to real-time and batch endpoints
                                                • 2. Configure deployment options and scaling

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                                                  Microsoft AI-300최고품질 시험덤프 공부자료 & AI-300최신 인증시험 공부자료

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                                                  최신 Microsoft Certified AI-300 무료샘플문제 (Q72-Q77):

                                                  질문 # 72
                                                  Hotspot Question
                                                  A team is building a generative AI agent by using Retrieval-Augmented Generation (RAG) in Microsoft Foundry.
                                                  The team frequently updates prompt content. The team must be able to track changes across contributors while avoiding full application redeployments.
                                                  You need to enable rapid prompt iteration with traceability. Applications consuming the agent must be able to use updated prompts without requiring redeployment.
                                                  What should you configure for each requirement? To answer, select the appropriate options in the answer area.
                                                  NOTE: Each correct selection is worth one point.

                                                  정답：

                                                  설명：


                                                  질문 # 73
                                                  You manage an Azure Machine Learning workspace
                                                  You build an Azure Machine Learning pipeline for image classification by using custom components. You need to define the interface, metadata, and code to execute components from a Python function. Which function should you use?

                                                  정답：D


                                                  질문 # 74
                                                  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.

                                                  정답：

                                                  설명：

                                                  Explanation:
                                                  For a customer support workload used across multiple regions, Global Standard deployment is the right choice: it routes each request to the nearest available Azure region automatically, reducing latency globally and providing the highest throughput and availability. For a marketing workload that must remain within a specific region due to data residency requirements, a Data Zone Standard or single-region deployment ensures all compute and data processing occurs within a defined geographic boundary, satisfying GDPR and local data sovereignty rules. Microsoft Foundry ' s deployment types are designed around exactly this trade-off:
                                                  Global routing for performance-critical multi-region workloads, and Data Zone or Regional isolation for data- residency-constrained workloads. Choosing the wrong deployment type can result in either compliance violations or unnecessary latency.
                                                  Microsoft Learn Reference Topic: Model deployment options in Microsoft Foundry - Global, Data Zone, and Regional deployment types


                                                  질문 # 75
                                                  You have an Azure Machine Learning workspace named Workspace 1 Workspace! has a registered Mlflow model named model 1 with PyFunc flavor You plan to deploy model1 to an online endpoint named endpoint1 without egress connectivity by using Azure Machine learning Python SDK vl You have the following code:

                                                  You need to add a parameter to the ManagedOnlineDeployment object to ensure the model deploys successfully Solution: Add the environment parameter.
                                                  Does the solution meet the goal?

                                                  정답：A


                                                  질문 # 76
                                                  A team schedules weekly retraining of a model using Azure ML pipelines. They also want retraining triggered automatically when production data significantly deviates from training data distribution, without duplicating pipeline logic. What should they implement?

                                                  정답：D

                                                  설명：
                                                  Using a single pipeline triggered by both a schedule and data drift alerts ensures consistent retraining logic and avoids duplication. This approach minimizes operational overhead and maintenance complexity. Creating multiple pipelines can lead to inconsistencies, duplicated code, and increased effort when updating retraining logic or dependencies.


                                                  질문 # 77
                                                  ......

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