New AI-300 Exam Objectives | AI-300 New Braindumps Files

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Microsoft AI-300 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: 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 2: 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. Define evaluation metrics and criteria
                • 2. Test for safety, accuracy, and relevance
                  Topic 3: Implement machine learning model lifecycle and operations25–30%- Register, version, and package models
                  • 1. Create reusable model packages
                    • 2. Manage model registry
                      - Deploy models to production
                      • 1. Configure deployment options and scaling
                        • 2. Deploy to real-time and batch endpoints
                          - Monitor and maintain models in production
                          • 1. Implement retraining and update workflows
                            • 2. Monitor data and model drift
                              - Orchestrate model training and experimentation
                              • 1. Track experiments and metrics
                                • 2. Create and manage pipelines
                                  Topic 4: Design and implement an MLOps infrastructure15–20%- Create and manage Machine Learning workspace resources and assets
                                  • 1. Configure workspace settings and security
                                    • 2. Manage compute targets, datastores, and environments
                                      - Implement infrastructure as code for Machine Learning
                                      • 1. Automate infrastructure provisioning
                                        • 2. Use Bicep or Azure CLI to deploy resources
                                          Topic 5: Design and implement a GenAIOps infrastructure20–25%- Set up Microsoft Foundry environment
                                          • 1. Manage compute and deployment resources
                                            • 2. Configure projects, connections, and security
                                              - Implement infrastructure for generative AI workloads
                                              • 1. Integrate with Azure services and tools
                                                • 2. Design scalable and secure architecture

                                                  >> New AI-300 Exam Objectives <<

                                                  PracticeMaterial Microsoft AI-300 Questions PDF Format

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                                                  Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions (Q48-Q53):

                                                  NEW QUESTION # 48
                                                  Hotspot Question
                                                  You manage a Microsoft Foundry project.
                                                  You are developing a solution to generate content based on text and images. The solution requires the ability to manage high-volume processing and avoid disruptions to the online workloads.
                                                  You need to deploy the solution.
                                                  Which deployment type and large language model (LLM) should you use? To answer, select the appropriate options in the answer area.
                                                  NOTE: Each correct selection is worth one point.

                                                  Answer:

                                                  Explanation:

                                                  Explanation:
                                                  Using Global provisioned deployment and a GPT-4o model is an excellent, industry-standard choice for this Microsoft AI Foundry project. This approach ensures you meet your high-volume processing and text-and-image generation requirements without degrading online workloads, offering both guaranteed latency and dedicated resources.
                                                  Box 1: GPT-4o
                                                  The GPT-4o Model is highly optimized for handling both text and image input. It provides the multi-modal reasoning necessary to generate contextually accurate content based on visual and textual datasets.
                                                  Box 2: Global provisioned
                                                  Provisioned Deployment Type: Provisioned throughput allows you to allocate dedicated compute capacity (tokens per minute). This guarantees performance and is explicitly designed to handle high-volume processing without disrupting your online workloads, as you will not be sharing constrained resources.
                                                  Global vs. Regional: Global provisioned deployments decouple capacity management from specific regions, providing the highest throughput limits and automatic access to resources with predictable costs. However, if your enterprise has strict European data residency regulations, choosing a Regional Provisioned deployment in a local region ensures your traffic and data are processed entirely within the EU.
                                                  Reference:
                                                  https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/provisioned-throughput


                                                  NEW QUESTION # 49
                                                  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.

                                                  Answer: A,B,E


                                                  NEW QUESTION # 50
                                                  A team provisions an Azure Machine Learning environment by triggering pull requests.
                                                  Deployments must be automated, auditable, and require approval before running.
                                                  You need to select a deployment automation tool.
                                                  Which tool should you use?

                                                  Answer: B

                                                  Explanation:
                                                  GitHub Actions is the best tool for this scenario.
                                                  Pull Request Integration: GitHub Actions is a native continuous integration and continuous delivery (CI/CD) platform built directly into GitHub. It can natively listen for repository events- such as triggering a workflow automatically when a pull request is opened, updated, or merged.
                                                  Required Approvals: It provides built-in governance through Environments. You can configure specific deployment environments (e.g., Production) to enforce required reviewers, ensuring a deployment cannot run until an authorized user manually approves it.
                                                  Auditable Logs: Every workflow execution, event trigger, and manual approval is tracked chronologically within GitHub. This provides a highly transparent, centralized audit trail for compliance requirements.
                                                  Reference:
                                                  https://learn.microsoft.com/en-us/azure/developer/github/github-actions


                                                  NEW QUESTION # 51
                                                  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?

                                                  Answer: C


                                                  NEW QUESTION # 52
                                                  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?

                                                  Answer: A


                                                  NEW QUESTION # 53
                                                  ......

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