PROFESSIONAL-MACHINE-LEARNING-ENGINEER · Question #183
PROFESSIONAL-MACHINE-LEARNING-ENGINEER Question #183: Real Exam Question with Answer & Explanation
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Question
You created an ML pipeline with multiple input parameters. You want to investigate the tradeoffs between different parameter combinations. The parameter options are: - Input dataset - Max tree depth of the boosted tree regressor - Optimizer learning rate You need to compare the pipeline performance of the different parameter combinations measured in F1 score, time to train, and model complexity. You want your approach to be reproducible, and track all pipeline runs on the same platform. What should you do?
Options
- A1. Use BigQueryML to create a boosted tree regressor, and use the hyperparameter tuning
- B1. Create a Vertex AI pipeline with a custom model training job as part of the pipeline. Configure
- C1. Create a Vertex AI Workbench notebook for each of the different input datasets.
- D1. Create an experiment in Vertex AI Experiments.
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