PROFESSIONAL-MACHINE-LEARNING-ENGINEER · Question #10
PROFESSIONAL-MACHINE-LEARNING-ENGINEER Question #10: Real Exam Question with Answer & Explanation
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Question
You work with a data engineering team that has developed a pipeline to clean your dataset and save it in a Cloud Storage bucket. You have created an ML model and want to use the data to refresh your model as soon as new data is available. As part of your CI/CD workflow, you want to automatically run a Kubeflow Pipelines training job on Google Kubernetes Engine (GKE). How should you architect this workflow?
Options
- AConfigure your pipeline with Dataflow, which saves the files in Cloud Storage.
- BUse App Engine to create a lightweight python client that continuously polls Cloud Storage for
- CConfigure a Cloud Storage trigger to send a message to a Pub/Sub topic when a new file is
- DUse Cloud Scheduler to schedule jobs at a regular interval.
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