Exam Professional Machine Learning Engineer topic 1 question 81 discussion

The Cloud Compose may be good consideration if you are involved in getting Google Data Engineer Cert App enging is relevant to Dev-Op Cert Pls. if you know a bit about ML Google Cloud, we are preparing to take Google ML Cert, if there is no specifically particular requirement in the question. We must emphasize on use of Vertext AI as much as possible.

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.

Went with B

I think it's D. B does not support Docker containers, does it?

Shouldn't it be A? https://cloud.google.com/appengine/docs/standard/scheduling-jobs-with-cron-yaml

Vote for B

Vote for B

D is the only option that provides scheduled model retraining

Serve Vertex AI Prediction, but the monitoring in the question is not the one of the answer B. (that is connected to the modeol). The correct answer is C.

Everything is possible on Vetex AI