Ultimate access to all questions.
You work for a retail company that is using a regression model built with BigQuery ML to predict product sales. This model is currently being used to serve online predictions to optimize inventory and sales strategies. Recently, you developed a new version of the model that uses a different architecture (custom model). Initial analysis revealed that both models are performing as expected in terms of prediction accuracy and latency. You now want to deploy the new version of the model to production and monitor its performance over the next two months. Your main objective is to minimize the impact on existing users and ensure a smooth transition. How should you deploy the model?
A
Import the new model to the same Vertex AI Model Registry as a different version of the existing model. Deploy the new model to the same Vertex AI endpoint as the existing model, and use traffic splitting to route 95% of production traffic to the BigQuery ML model and 5% of production traffic to the new model.
B
Import the new model to the same Vertex AI Model Registry as the existing model. Deploy the models to one Vertex AI endpoint. Route 95% of production traffic to the BigQuery ML model and 5% of production traffic to the new model.
C
Import the new model to the same Vertex AI Model Registry as the existing model. Deploy each model to a separate Vertex AI endpoint.
D
Deploy the new model to a separate Vertex AI endpoint. Create a Cloud Run service that routes the prediction requests to the corresponding endpoints based on the input feature values.