Microsoft AI-300 Quizfragen Und Antworten - AI-300 Antworten

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Microsoft AI-300 Exam Syllabus Topics:

SectionObjectives
Implement secure and scalable AI systems- Security and governance
  • 1. Identity and access management for AI services
    • 2. Data privacy and compliance considerations
      - Scalability and performance optimization
      • 1. Autoscaling AI workloads
        • 2. Cost optimization strategies
          Operationalizing machine learning solutions- Deployment and monitoring
          • 1. Monitor performance and drift
            • 2. Deploy models to endpoints
              - ML lifecycle management
              • 1. Model training and evaluation in Azure Machine Learning
                • 2. Model versioning and registry usage
                  Plan and design AI solutions using Azure AI services- Requirements gathering and solution architecture
                  • 1. Select appropriate Azure AI services
                    • 2. Identify business requirements for AI solutions
                      - Responsible AI design
                      • 1. Responsible AI mitigation strategies
                        • 2. Fairness, transparency, and accountability considerations
                          Design and implement generative AI solutions- RAG (Retrieval Augmented Generation) solutions
                          • 1. Knowledge grounding and retrieval design
                            • 2. Vector search integration
                              - Large language model integration
                              • 1. Use Azure OpenAI Service capabilities
                                • 2. Prompt engineering and prompt flow design

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                                  AI-300 Antworten & AI-300 Zertifizierungsfragen

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                                  Microsoft Operationalizing Machine Learning and Generative AI Solutions AI-300 Prüfungsfragen mit Lösungen (Q58-Q63):

                                  58. Frage
                                  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 Al (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.

                                  Antwort:

                                  Begründung:

                                  Explanation:


                                  59. Frage
                                  Drag and Drop Question
                                  You complete the fine-tuning of a generative model in Microsoft Foundry. The fine-tuned model now appears as a new model variant in your development environment.
                                  The deployment process must ensure that proper validation and control is maintained.
                                  You need to promote the fine-tuned model from development to production.
                                  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.

                                  Antwort:

                                  Begründung:


                                  60. Frage
                                  You have a Microsoft Foundry project and a CSV file stored in Azure Blob Storage.
                                  You plan to add the CSV file as the grounding data to the project for RAG by using the file data type.
                                  You need to specify the URL schema to designate the blob location.
                                  Which URL schema should you specify?

                                  Antwort: B

                                  Begründung:
                                  wasbs (Windows Azure Storage Blob Secure) is the officially supported URL scheme used by the underlying Azure Machine Learning and Microsoft Foundry data infrastructure to securely access and locate storage files directly from an Azure Blob Storage account. A typical URI format looks like: wasbs://<containername>@<accountname>.blob.core.windows.net/<path_to_data>.csv.
                                  Reference:
                                  https://learn.microsoft.com/en-us/azure/machine-learning/how-to-create-data-assets


                                  61. Frage
                                  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.

                                  Antwort:

                                  Begründung:

                                  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


                                  62. Frage
                                  A team runs training jobs by using multiple Azure Machine Learning pipelines.
                                  The team must ensure that all runs use the same Python packages and system libraries. The solution must allow dependency updates to be versioned without modifying training code.
                                  You need to configure the workspace so that runtime dependencies are consistent and reusable.
                                  Which four 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.

                                  Antwort:

                                  Begründung:

                                  Explanation:
                                  To ensure runtime dependencies are consistent and reusable, first create a conda.yaml or requirements.txt file that lists all Python packages and system libraries required by your training code - this file is the single source of truth for your runtime. Next, create an Environment object using the Azure ML Python SDK v2 with a name and reference to the conda.yaml file, specifying the base Docker image. Then register the Environment by calling ml_client.environments.create_or_update, which publishes it to the workspace registry with an auto-incremented version. Finally, reference the registered environment by name and version in all pipeline job steps. Azure ML will build or retrieve the cached Docker image and use it as the execution container. This approach means updating dependencies only requires modifying the conda.yaml and registering a new version - training code remains unchanged.
                                  Microsoft Learn Reference Topic: Create and manage Azure Machine Learning environments - Reusable curated environments


                                  63. Frage
                                  ......

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