Google Professional Machine Learning Engineer
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Ultimate access to all questions.
You are tasked with building an ML pipeline on Google Cloud for data processing, model training, and model deployment. Your solution involves different Google Cloud services such as Cloud Storage and Vertex AI. Each task in your pipeline has been individually coded and you anticipate a high frequency of new data files being added to your Cloud Storage bucket. Now, you need an orchestration layer to manage these tasks efficiently. This orchestration pipeline should only run when new files are detected in the Cloud Storage bucket to avoid unnecessary computations and minimize costs. What approach should you take to achieve this?
A
Create a pipeline in Vertex AI Pipelines. Configure the first step to compare the contents of the bucket to the last time the pipeline was run. Use the scheduler API to run the pipeline periodically.
B
Create a Cloud Function that uses a Cloud Storage trigger and deploys a Cloud Composer directed acyclic graph (DAG).
C
Create a pipeline in Vertex AI Pipelines. Create a Cloud Function that uses a Cloud Storage trigger and deploys the pipeline.
D
Deploy a Cloud Composer directed acyclic graph (DAG) with a GCSObjectUpdateSensor class that detects when a new file is added to the Cloud Storage bucket.