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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?

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Suggested Answer: C

Contribute your Thoughts:

Adaline
6 days ago
I think option C is the best approach. Setting the hyperparameters as conditional on the training method makes the most sense to me.
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German
6 days ago
That makes sense. We can optimize both model architecture and hyperparameters that way.
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Jamal
9 days ago
I disagree. We should run one hypertuning job for 100 trials and set conditional hyperparameters.
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German
19 days ago
I think we should run two separate hypertuning jobs to compare linear regression and DNN.
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