You create an Azure Machine Learning workspace and are implementing hyperparameter tuning for a model training run from a notebook.

You must configure a Bandit termination policy with the following behavior: If the primary metric (AUC) is 0.8 at the evaluation intervals, any run where the primary metric falls below 0.66 should be terminated.

Which Bandit termination policy configuration should you use?