Google Cloud Professional Machine Learning Engineer — Question 325

You have recently developed a new ML model in a Jupyter notebook. You want to establish a reliable and repeatable model training process that tracks the versions and lineage of your model artifacts. You plan to retrain your model weekly. How should you operationalize your training process?

Answer options

• A. 1. Create an instance of the CustomTrainingJob class with the Vertex AI SDK to train your model. 2. Using the Notebooks API, create a scheduled execution to run the training code weekly.
• B. 1. Create an instance of the CustomJob class with the Vertex AI SDK to train your model. 2. Use the Metadata API to register your model as a model artifact. 3. Using the Notebooks API, create a scheduled execution to run the training code weekly.
• C. 1. Create a managed pipeline in Vertex AI Pipelines to train your model by using a Vertex AI CustomTrainingJobOp component. 2. Use the ModelUploadOp component to upload your model to Vertex AI Model Registry. 3. Use Cloud Scheduler and Cloud Run functions to run the Vertex AI pipeline weekly.
• D. 1. Create a managed pipeline in Vertex AI Pipelines to train your model using a Vertex AI HyperParameterTuningJobRunOp component. 2. Use the ModelUploadOp component to upload your model to Vertex AI Model Registry. 3. Use Cloud Scheduler and Cloud Run functions to run the Vertex AI pipeline weekly.

Correct answer: C

Explanation

The correct answer is C because it describes a complete approach to automating the training process through a managed pipeline, which includes using the appropriate components for training and model uploading, as well as scheduling the execution. Options A and B lack a managed pipeline and do not leverage Cloud Scheduler effectively, while option D uses a HyperParameterTuningJobRunOp, which is not necessary for the basic weekly retraining process.