100% Free AI-300 Practice Exam Questions and Answers

An organization validates generative AI applications during CI/CD Microsoft Foundry. Evaluation must run automatically and block releases when quality thresholds are NOT met. Manual evaluation is no longer acceptable. Evaluation must use both predefined quality metrics and custom safety checks. You need to implement an automated evaluation workflow that supports both built-in and custom metrics. What should you do? 

A biomedical research company plans to enroll people in an experimental medical treatment trial.You create and train a binary classification model to support selection and admission of patients to the trial.The model includes the following features: Age, Gender, and Ethnicity.The model returns different performance metrics for people from different ethnic groups.You need to use Fairlearn to mitigate and minimize disparities for each category in the Ethnicity feature.Which technique and constraint should you use? To answer, select the appropriate options in the answer area.NOTE: Each correct selection is worth one point.Please review the explanation below to find the correct answer.Do you know the answer?

A data science team trains a classification model that predicts loan approval outcomes. Before registering the model, the team must ensure the following: Predictions must not disproportionately impact protected groups. Prediction errors can be evaluated across different data segments. You need to assess whether the model meets Responsible AI expectations. Which two approaches should you use? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point. Choose two.

-A team is standardizing MLOps practices by using automated deployments.The team requires infrastructure to be defined declaratively and deployed through automation pipelines.You need to configure infrastructure deployment.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.Please review the explanation below to find the correct answer.Do you know the answer?

You have an Azure Machine Learning workspace named Workspace^ Workspace1 has a registered MLflowmodel named model1 with PyFunc flavor. You plan to deploy model1 to an online endpoint named endpoint1without egress connectivity by using Azure Machine Learning Python SDK v2. You have the following code:You need to add a parameter to the ManagedOnlineDeployment object to ensure the model deployssuccessfully.Solution: Add the code_path parameter.Does the solution meet the goal?