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Google Professional Machine Learning Engineer Professional-Machine-Learning-Engineer Question # 33 Topic 4 Discussion

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Google Professional Machine Learning Engineer Professional-Machine-Learning-Engineer Question # 33 Topic 4 Discussion

Professional-Machine-Learning-Engineer Exam Topic 4 Question 33 Discussion:
Question #: 33
Topic #: 4

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?
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 Al Pipelines to train your model by using a Vertex Al CustomTrainingJoOp component.

2. Use the ModelUploadOp component to upload your model to Vertex Al Model Registry.

3. Use Cloud Scheduler and Cloud Functions to run the Vertex Al pipeline weekly.

D.

1. Create a managed pipeline in Vertex Al Pipelines to train your model using a Vertex Al HyperParameterTuningJobRunOp component.

2. Use the ModelUploadOp component to upload your model to Vertex Al Model Registry.

3. Use Cloud Scheduler and Cloud Functions to run the Vertex Al pipeline weekly.

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Actual exam question for Google Professional-Machine-Learning-Engineer exam by Nyx4748 at Mar 17, 2026, 4:04:18 AM

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