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PROFESSIONAL-MACHINE-LEARNING-ENGINEER · Question #321

You recently deployed an image classification model on Google Cloud. You used Cloud Build to build a CI/CD pipeline for the model. You need to ensure that the model stays up-to-date with data and…

The correct answer is B. Configure a Git repository trigger in Cloud Build to initiate retraining when there are new code. Combining Git repository triggers in Cloud Build for code changes and Pub/Sub triggers for new data ensures that the retraining process responds dynamically to both relevant changes. This setup supports efficient and automated CI/CD for ML models without relying on fixed…

Submitted by akirajp· Apr 18, 2026ML pipeline operationalization

Question

You recently deployed an image classification model on Google Cloud. You used Cloud Build to build a CI/CD pipeline for the model. You need to ensure that the model stays up-to-date with data and code changes by using an efficient retraining process. What should you do?

Options

  • AUse Cloud Run functions to monitor data drift in real time and trigger a Vertex AI Training job to
  • BConfigure a Git repository trigger in Cloud Build to initiate retraining when there are new code
  • CUse Cloud Scheduler to initiate a daily retraining job in Vertex AI Pipelines.
  • DConfigure Cloud Composer to orchestrate a weekly retraining job that includes data extraction

How the community answered

(54 responses)
  • A
    17% (9)
  • B
    70% (38)
  • C
    9% (5)
  • D
    4% (2)

Explanation

Combining Git repository triggers in Cloud Build for code changes and Pub/Sub triggers for new data ensures that the retraining process responds dynamically to both relevant changes. This setup supports efficient and automated CI/CD for ML models without relying on fixed schedules or manual checks.

Topics

#MLOps#CI/CD#Retraining#Cloud Build

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