Topic 1 Question 174
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 correlations between the features in the near future. You also expect to receive a large volume of prediction requests. You plan to use Vertex AI Model Monitoring for drift detection and you want to minimize the cost. What should you do?
Use the features for monitoring. Set a monitoring-frequency value that is higher than the default.
Use the features for monitoring. Set a prediction-sampling-rate value that is closer to 1 than 0.
Use the features and the feature attributions for monitoring. Set a monitoring-frequency value that is lower than the default.
Use the features and the feature attributions for monitoring. Set a prediction-sampling-rate value that is closer to 0 than 1.
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- 正解だと思う選択肢: D
Given the need to minimize costs while addressing changes in feature distributions and correlations, option D - "Use the features and the feature attributions for monitoring. Set a prediction-sampling-rate value that is closer to 0 than 1" seems to be a reasonable choice. This option allows monitoring both features and feature attributions, offering insights into changes in feature importance, while the lower prediction-sampling-rate helps manage costs by monitoring a subset of predictions. It's a trade-off between cost efficiency and the need for effective drift detection
👍 1pikachu0072024/01/11 - 正解だと思う選択肢: D
if we expect a large volume of prediction requests then pick D. if we expect the changes to be infrequent then C https://cloud.google.com/vertex-ai/docs/model-monitoring/overview#considerations
👍 1BlehMaks2024/01/12
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