The correct option is primary_metric='AUC_weighted'.

In Azure Machine Learning Automated ML you set the metric that the service should optimize by using the primary_metric='AUC_weighted' parameter in the AutoMLConfig. This tells the automated search to rank and select models based on the weighted area under the ROC curve.

AUC_weighted measures the weighted average of class wise AUC and is useful when you want a single summary metric for multiclass or imbalanced classification. Setting primary_metric aligns the AutoML optimization objective with that metric.

task='AUC_weighted' is incorrect because the task parameter specifies the problem type such as 'classification' or 'regression' and it is not used to choose the evaluation metric.

compute_target='AUC_weighted' is incorrect because compute_target designates the compute resource where the experiment runs and it cannot be a metric name.

label_column_name='AUC_weighted' is incorrect because label_column_name should be the name of the target column in your dataset and it does not control which metric AutoML optimizes.

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