Google Professional Machine Learning Engineer
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As a developer and data scientist at a medium-sized company utilizing Vertex AI / AI Platform, you've updated an Auto ML model and wish to deploy it to production while keeping both the old and new versions active simultaneously. The new version should handle only a small fraction of the traffic. The solution must minimize operational overhead and ensure seamless transition between model versions without downtime. Which two actions should you take? (Choose two)
Explanation:
The correct approach involves deploying your model to an existing endpoint (Option B) and adjusting the Traffic split percentage (Option C) to ensure the new version handles only a small fraction of the traffic. This method minimizes operational overhead and ensures a seamless transition without downtime. Option A is incorrect as creating a Docker container image is unnecessary with AutoML. Option D is not applicable here since Canary Deployment with Cloud Build is a CI/CD pipeline process, which isn't required in this managed environment. Option E would increase operational complexity and is not necessary for achieving the goal. For more details, visit: Google Cloud Vertex AI Documentation.