AI-300考證，AI-300考古題

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Microsoft AI-300 Exam Syllabus Topics:

SectionWeightObjectives
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. Track usage, performance, and errors
        • 2. Implement logging and alerting
          Implement machine learning model lifecycle and operations25–30%- Orchestrate model training and experimentation
          • 1. Create and manage pipelines
            • 2. Track experiments and metrics
              - Monitor and maintain models in production
              • 1. Implement retraining and update workflows
                • 2. Monitor data and model drift
                  - Deploy models to production
                  • 1. Deploy to real-time and batch endpoints
                    • 2. Configure deployment options and scaling
                      - Register, version, and package models
                      • 1. Create reusable model packages
                        • 2. Manage model registry
                          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. Use Bicep or Azure CLI to deploy resources
                                • 2. Automate infrastructure provisioning
                                  Optimize generative AI systems and model performance15–20%- Optimize model selection and configuration
                                  • 1. Choose appropriate models and parameters
                                    • 2. Tune prompts and generation settings
                                      - Improve efficiency and cost-effectiveness
                                      • 1. Optimize inference and deployment
                                        • 2. Manage resource utilization
                                          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

                                                  >> AI-300考證 <<

                                                  AI-300考古題 & 新版AI-300題庫

                                                  想參加Microsoft的AI-300認證考試嗎？你正在因為考試很難而發愁嗎？想報名參加考試，但是又擔心通過不了。你現在有這樣的心情嗎？沒關係，安心地報名吧。因為你只要用了Fast2test的資料，再難的考試也不是問題。即使你對通過考試一點信心也沒有，Fast2test的AI-300考古題也可以保證你一次就輕鬆成功。覺得不可思議嗎？你可以來Fast2test的網站瞭解更多的資訊。另外，你還可以先試用AI-300考古題的一部分。這樣的話你肯定就會知道，這個參考資料是你順利通過考試的保障。

                                                  最新的 Microsoft Certified AI-300 免費考試真題 (Q85-Q90):

                                                  問題 #85
                                                  Hotspot Question
                                                  You manage an Azure Machine Learning workspace named workspace1 by using the Python SDK v2.
                                                  The default datastore of workspace1 contains a folder named sample_data. The folder structure contains the following content:

                                                  You write Python SDK v2 code to materialize the data from the files in the sample_data folder into a Pandas data frame.
                                                  You need to complete the Python SDK v2 code to use the MLTable folder as the materialization blueprint.
                                                  How should you complete the code? To answer, select the appropriate options in the answer area.
                                                  NOTE: Each correct selection is worth one point

                                                  答案：

                                                  解題說明：


                                                  問題 #86
                                                  A team maintains Infrastructure as Code (IaC) templates to provision Azure Machine Learning resources.
                                                  Provisioning must be triggered by changes in the templates and executed without manual intervention.
                                                  You need to automate resource provisioning.
                                                  Which action should you take for each requirement? To answer, move the appropriate actions to the correct requirements. You may use each action 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.

                                                  答案：

                                                  解題說明：

                                                  Explanation:
                                                  Azure Machine Learning infrastructure can be described as Infrastructure as Code using ARM templates, Bicep, or Terraform. To make provisioning fully automated, two separate concerns must be addressed. First, the trigger: a CI/CD system like GitHub Actions or Azure DevOps Pipelines must detect changes to IaC template files in the repository, configured through a branch-push or pull-request trigger. Second, the execution: the CI/CD pipeline runs the provisioning commands such as az ml workspace create or bicep deploy non-interactively using a service principal or managed identity, eliminating the need for a human to run scripts manually. Together, these two mechanisms deliver the GitOps-style automation that modern MLOps practices require, ensuring every template change automatically results in a consistent, auditable infrastructure update.
                                                  Microsoft Learn Reference Topic: Automate Azure Machine Learning with GitHub Actions - CI/CD for ML infrastructure


                                                  問題 #87
                                                  You create an Azure Machine Learning workspace. You train an MLflow-formatted regression model by using tabular structured data.
                                                  You must use a Responsible AI dashboard to assess the model.
                                                  You need to use the Azure Machine Learning studio UI to generate the Responsible AI dashboard.
                                                  What should you do first?

                                                  答案：C

                                                  解題說明：
                                                  The first step you must take is to register the model with the workspace.
                                                  To access the no-code, guided wizard for generating a Responsible AI dashboard directly within the Azure Machine Learning studio UI, the trained model must first exist as a recognized asset inside your workspace's model registry.
                                                  Reference:
                                                  https://learn.microsoft.com/en-us/azure/machine-learning/how-to-responsible-ai-dashboard


                                                  問題 #88
                                                  -
                                                  You review the following Azure CLI command and the relevant Bicep excerpt.

                                                  (Non-relevant sections are omitted.)
                                                  You need to validate what the snippet will do before it is merged. For each of the following statements, select Yes if the statement is true. Otherwise, select No.
                                                  NOTE: Each correct selection is worth one point.

                                                  答案：

                                                  解題說明：

                                                  Explanation:
                                                  The command deploys the resources into an existing resource group named rg-foundry-dev: Yes The system-assigned managed identity defined in the template will automatically be inherited by all Microsoft Foundry projects: No To deploy this template to a different subscription, you must modify the Bicep file to include a subscriptionId parameter: No The first statement is Yes . The command uses az deployment group create --resource-group rg-foundry-dev, which performs an Azure Resource Manager deployment at resource-group scope . Microsoft documents that the target resource group must already exist; if it does not, it must be created before running the resource- group deployment.
                                                  The second statement is No . The Bicep declaration assigns a system-assigned managed identity to the Foundry resource itself . A Microsoft Foundry project is a child resource and can have its own managed identity . Microsoft explicitly shows project creation with " identity " : { " type " : " SystemAssigned " } and separately discusses assigning permissions to a project ' s managed identity. Therefore, the parent ' s system- assigned identity is not automatically inherited as the identity of every project.
                                                  The third statement is No . Because targetScope = ' resourceGroup ' , the same Bicep template can be deployed to a resource group in another subscription without adding a subscriptionId parameter. Azure CLI supports the global --subscription argument, or the active subscription can be changed with az account set.


                                                  問題 #89
                                                  A team validates a generative AI application that produces free-form text responses by using Microsoft Foundry SDK.
                                                  The evaluation dataset is registered in the Microsoft Foundry environment.
                                                  You need to configure a safety evaluation pipeline that reliably evaluates model outputs for harmful content.
                                                  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.

                                                  答案：

                                                  解題說明：

                                                  Explanation:
                                                  Correct sequence:
                                                  * Install the Foundry SDK project client locally.
                                                  * Configure safety evaluators.
                                                  * Submit an evaluation to the Microsoft Foundry project in the cloud.
                                                  The first step is to install and configure the Microsoft Foundry SDK project client . Microsoft documents the Foundry project client as the programmatic entry point for authenticating to a Foundry project and accessing its evaluation capabilities. The client typically uses DefaultAzureCredential, avoiding embedded credentials while enabling access to project resources.
                                                  Next, configure the safety evaluators that correspond to the risks that must be measured. Microsoft Foundry provides built-in safety evaluators for categories including Violence, Sexual content, Self-harm, and Hate
                                                  /Unfairness . These evaluators analyze generated responses and return structured safety assessments rather than relying on subjective manual review.
                                                  Finally, submit the evaluation to the Microsoft Foundry project in the cloud . Current Foundry evaluation workflows define the evaluator configuration, create an evaluation, and start an evaluation run in the project.
                                                  Results are persisted in Foundry for comparison, auditing, and CI/CD quality gates.
                                                  Uploading evaluation data is unnecessary because the scenario explicitly states that the dataset is already registered . Microsoft documentation specifically instructs users to skip dataset upload when a registered dataset already exists. Free-form text upload is also inappropriate because structured evaluation datasets use supported schemas such as JSONL or CSV.
                                                  Study Guide Reference: Implement generative AI quality assurance and observability - Foundry evaluations, safety evaluators, evaluation datasets, cloud evaluation runs, and harmful-content measurement.


                                                  問題 #90
                                                  ......

                                                  選擇捷徑、使用技巧是為了更好地獲得成功。如果你想獲得一次就通過AI-300認證考試的保障，那麼Fast2test的AI-300考古題是你唯一的、也是最好的選擇。這絕對是一個讓你禁不住讚美的考古題。你不可能找到比它更好的考試相關的資料了。這個考古題可以讓你更準確地瞭解考試的出題點，從而讓你更有目的地學習相關知識。另外，如果你實在沒有準備考試的時間，那麼你只需要記好這個考古題裏的試題和答案。因為這個考古題包括了真實考試中的所有試題，所以只是這樣你也可以通過考試。

                                                  AI-300考古題: https://tw.fast2test.com/AI-300-premium-file.html