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Question 89/139
You developed a custom model by using Vertex Al to predict your application's user churn rate You are using Vertex Al Model Monitoring for skew detection The training data stored in BigQuery contains two sets of features - demographic and behavioral You later discover that two separate models trained on each set perform better than the original model You need to configure a new model mentioning pipeline that splits traffic among the two models You want to use the same prediction-sampling-rate and monitoring-frequency for each model You also want to minimize management effort What should you do?
Correct Answer: D
* Option A is incorrect because it does not separate the training dataset into two tables based on the features, which is necessary to train the two models separately and accurately.
* Option B is incorrect because it does not separate the training dataset into two tables based on the features, and because it uses the same monitoring-config-from parameter for both models, which would not account for the different feature selections.
* Option C is incorrect because it deploys the models to two separate endpoints, which would increase the management effort and complexity of the pipeline.
* Option D is correct because it separates the training dataset into two tables based on the features, which would enable the two models to be trained separately and accurately. It also deploys both models to the same endpoint, which would simplify the pipeline and reduce the management effort. It also submits a Vertex Al Model Monitoring job with a monitoring-config-from parameter that accounts for the model IDs and training datasets, which would enable the skew detection to work properly for each model.
* Option B is incorrect because it does not separate the training dataset into two tables based on the features, and because it uses the same monitoring-config-from parameter for both models, which would not account for the different feature selections.
* Option C is incorrect because it deploys the models to two separate endpoints, which would increase the management effort and complexity of the pipeline.
* Option D is correct because it separates the training dataset into two tables based on the features, which would enable the two models to be trained separately and accurately. It also deploys both models to the same endpoint, which would simplify the pipeline and reduce the management effort. It also submits a Vertex Al Model Monitoring job with a monitoring-config-from parameter that accounts for the model IDs and training datasets, which would enable the skew detection to work properly for each model.
- Question List (139q)
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