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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 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.

An Azure Machine Learning workspace processes sensitive training data. The workspace must NOT be accessible from the public internet. You need to restrict network access. Which configuration should you implement?

A team plans to deploy a large foundation model in Microsoft Foundry as part of a new enterprise AI capability. Different business units across the team's organization will access the model from various internal applications. You need to deploy a foundation model by minimizing latency. Which deployment type should you use?