You developed a custom model by using Vertex AI to forecast the sales of your company's products based on historical transactional data. You anticipate changes in the feature distributions and the…

Setting a monitoring-frequency value that is higher than the default means more frequent checks, which would significantly increase monitoring costs, contradicting the goal of cost minimization.

Setting a prediction-sampling-rate value closer to 1 means monitoring a larger percentage of predictions, which would substantially increase the cost of the monitoring job.

While using features and feature attributions is robust for drift detection, setting a higher monitoring-frequency value would lead to increased costs, failing to meet the cost minimization requirement.

Using both features and feature attributions for monitoring provides comprehensive drift detection, especially when changes in feature correlations are anticipated. To minimize cost with a large volume of predictions, setting a low prediction-sampling-rate reduces the amount of data processed by the monitoring job, thus lowering expenses without sacrificing essential detection capabilities.