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PROFESSIONAL-MACHINE-LEARNING-ENGINEER · Question #290
You are creating an ML pipeline for data processing, model training, and model deployment that uses different Google Cloud services. You have developed code for each individual task, and you expect…
The correct answer is C. Create a pipeline in Vertex AI Pipelines. Create a Cloud Function that uses a Cloud Storage. Cloud Function to be triggered by Cloud storage trigger, and then deploy the Vertex AI pipeline.
Submitted by lars.no· Apr 18, 2026ML pipeline operationalization
Question
You are creating an ML pipeline for data processing, model training, and model deployment that uses different Google Cloud services. You have developed code for each individual task, and you expect a high frequency of new files. You now need to create an orchestration layer on top of these tasks. You only want this orchestration pipeline to run if new files are present in your dataset in a Cloud Storage bucket. You also want to minimize the compute node costs. What should you do?
Options
- ACreate a pipeline in Vertex AI Pipelines. Configure the first step to compare the contents of the
- BCreate a Cloud Function that uses a Cloud Storage trigger and deploys a Cloud Composer
- CCreate a pipeline in Vertex AI Pipelines. Create a Cloud Function that uses a Cloud Storage
- DDeploy a Cloud Composer directed acyclic graph (DAG) with a GCSObjectUpdateSensor class
How the community answered
(34 responses)- A3% (1)
- B21% (7)
- C68% (23)
- D9% (3)
Explanation
Cloud Function to be triggered by Cloud storage trigger, and then deploy the Vertex AI pipeline.
Topics
#ML Pipeline Orchestration#Serverless Triggers#Cost Optimization#Vertex AI Pipelines
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