PROFESSIONAL-MACHINE-LEARNING-ENGINEER · Question #253
PROFESSIONAL-MACHINE-LEARNING-ENGINEER Question #253: Real Exam Question with Answer & Explanation
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Question
Your team is training a large number of ML models that use different algorithms, parameters, and datasets. Some models are trained in Vertex AI Pipelines, and some are trained on Vertex AI Workbench notebook instances. Your team wants to compare the performance of the models across both services. You want to minimize the effort required to store the parameters and metrics. What should you do?
Options
- AImplement an additional step for all the models running in pipelines and notebooks to export
- BCreate a Vertex AI experiment. Submit all the pipelines as experiment runs. For models trained
- CImplement all models in Vertex AI Pipelines Create a Vertex AI experiment, and associate all
- DStore all model parameters and metrics as model metadata by using the Vertex AI Metadata API.
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