Google Cloud Certified Professional Machine Learning Engineer Practice Mode

Community Discussion

Written by other learners. Comments can disagree with the marked answer — check the official documentation when in doubt.

• Chose C1

  why its called kubeflow pipeline in OPtion A, not Vertex AI pipeline, coz KFP can be generic, on any K8s or GKE or so.

• Chose A1

  Req: retrain the model every month+ Google-recommended best practice+ end-to-end architecture A. Configure Kubeflow Pipelines to schedule your multi-step workflow from training to deploying your model. : Supports all above B. Use a model trained and deployed on BigQuery ML, and trigger retraining with the scheduled query feature in BigQuery : Why BigQuery ML when vertexAI/kubflow can handle end to end. BigQuery ML+ traigger only initiate the code run. C. Write a Cloud Functions script that launches a training and deploying job on AI Platform that is triggered by Cloud Scheduler. : Not recommended by google for end to end ML D. Use Cloud Composer to programmatically schedule a Dataflow job that executes the workflow from training to deploying your model. : Not recommended by google for end to end ML. what if model fails? matrix monitor?

• Chose A1

  A) Kubeflow Pipelines is the answer.