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Google Cloud Certified Professional Machine Learning Engineer · Google Cloud
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B) Vertex AI Pipelines
B. Vertext AI also supports Docker container https://cloud.google.com/vertex-ai/docs/training/containers-overview
A custom container is a Docker image that you create to run your training application. By running your machine learning (ML) training job in a custom container, you can use ML frameworks, non-ML dependencies, libraries, and binaries that are not otherwise supported on Vertex AI. so we need vertex ai custom container for docker container. Thus option A and B are omitted . App Engine allows developers to focus on what they do best: writing code. Based on Compute Engine, the App Engine flexible environment automatically scales your app up and down while also balancing the load. Customizable infrastructure - App Engine flexible environment instances are Compute Engine virtual machines, which means that you can take advantage of custom libraries, use SSH for debugging, and deploy your own Docker containers.