Google Exam Professional-Machine-Learning-Engineer Topic 4 Question 81 Discussion

Actual exam question for Google's Google Professional Machine Learning Engineer exam

Question #: 81
Topic #: 4

[All Google Professional Machine Learning Engineer Questions]

You developed a Python module by using Keras to train a regression model. You developed two model architectures, linear regression and deep neural network (DNN). within the same module. You are using the -- raining_method argument to select one of the two methods, and you are using the Learning_rate-and num_hidden_layers arguments in the DNN. You plan to use Vertex Al's hypertuning service with a Budget to perform 100 trials. You want to identify the model architecture and hyperparameter values that minimize training loss and maximize model performance What should you do?

ARun one hypertuning job for 100 trials. Set num hidden_layers as a conditional hypetparameter based on its parent hyperparameter training_mothod. and set learning rate as a non-conditional hyperparameter

BRun two separate hypertuning jobs. a linear regression job for 50 trials, and a DNN job for 50 trials Compare their final performance on a
common validation set. and select the set of hyperparameters with the least training loss

CRun one hypertuning job for 100 trials Set num_hidden_layers and learning_rate as conditional hyperparameters based on their parent hyperparameter training method.

DRun one hypertuning job with training_method as the hyperparameter for 50 trials Select the architecture with the lowest training loss. and further hypertune It and its corresponding hyperparameters for 50 trials

Show Suggested Answer

Suggested Answer: C

by Bok at Jun 28, 2024, 08:12 AM

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Carin

3 months ago

Option A looks tempting, but setting the number of hidden layers as a conditional hyperparameter seems like the way to go. Gotta love that Vertex AI magic!

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Amalia

3 months ago

I'm leaning towards option D. Doing a 50-trial run to select the architecture, then fine-tuning it, seems like a good compromise between exploration and exploitation.

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