PROFESSIONAL-MACHINE-LEARNING-ENGINEER · Question #144
PROFESSIONAL-MACHINE-LEARNING-ENGINEER Question #144: Real Exam Question with Answer & Explanation
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Question
You have recently created a proof-of-concept (POC) deep learning model. You are satisfied with the overall architecture, but you need to determine the value for a couple of hyperparameters. You want to perform hyperparameter tuning on Vertex AI to determine both the appropriate embedding dimension for a categorical feature used by your model and the optimal learning rate. You configure the following settings: - For the embedding dimension, you set the type to INTEGER with a minValue of 16 and maxValue of 64. - For the learning rate, you set the type to DOUBLE with a minValue of 10e-05 and maxValue of 10e-02. You are using the default Bayesian optimization tuning algorithm, and you want to maximize model accuracy. Training time is not a concern. How should you set the hyperparameter scaling for each hyperparameter and the maxParallelTrials?
Options
- AUse UNIT_LINEAR_SCALE for the embedding dimension, UNIT_LOG_SCALE for the learning
- BUse UNIT_LINEAR_SCALE for the embedding dimension, UNIT_LOG_SCALE for the learning
- CUse UNIT_LOG_SCALE for the embedding dimension, UNIT_LINEAR_SCALE for the learning
- DUse UNIT_LOG_SCALE for the embedding dimension, UNIT_LINEAR_SCALE for the learning
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