Topic 1 Question 291
You work for a hospital. You received approval to collect the necessary patient data, and you trained a Vertex AI tabular AutoML model that calculates patients' risk score for hospital admission. You deployed the model. However, you're concerned that patient demographics might change over time and alter the feature interactions and impact prediction accuracy. You want to be alerted if feature interactions change, and you want to understand the importance of the features for the predictions. You want your alerting approach to minimize cost. What should you do?
Create a feature drift monitoring job. Set the sampling rate to 1 and the monitoring frequency to weekly.
Create a feature drift monitoring job. Set the sampling rate to 0.1 and the monitoring frequency to weekly.
Create a feature attribution drift monitoring job. Set the sampling rate to 1 and the monitoring frequency to weekly.
Create a feature attribution drift monitoring job. Set the sampling rate to 0.1 and the monitoring frequency to weekly.
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コメント(4)
- 正解だと思う選択肢: D
specifically concerned about changes in feature interactions and their impact on predictions. Feature attribution drift monitoring directly addresses this by tracking how the importance of different features (and their interactions) changes over time.
https://cloud.google.com/vertex-ai/docs/model-monitoring/monitor-explainable-ai
👍 2Omi_040402024/12/10 - 正解だと思う選択肢: B
This is feature drift (features are changing) and not feature attribution drift (features are having different effects on the prediction).
👍 1JDpmle20242024/10/25 - 正解だと思う選択肢: D
Why other options are less suitable:
A and B (Feature Drift Monitoring): While basic feature drift monitoring can detect changes in feature distributions, it doesn't directly address your concern about changes in feature interactions and their impact on predictions. C (Sampling Rate of 1): Analyzing 100% of the prediction requests for feature attribution drift can be expensive, especially if you have high traffic.
👍 1AB_C2024/11/27
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