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Google Exam Professional Machine Learning Engineer Topic 4 Question 81 Discussion

Actual exam question for Google's Professional Machine Learning Engineer exam
Question #: 81
Topic #: 4
[All 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:

Carin
5 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
5 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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Hubert
5 months ago
Haha, I bet the developer who wrote this question has a lot of experience with hyperparameter tuning. It's like a brain teaser!
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Jacki
4 months ago
D
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Gussie
4 months ago
B
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Tamekia
4 months ago
A
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Kirk
4 months ago
D
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Nelida
4 months ago
C
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Dorinda
4 months ago
B
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Arlene
4 months ago
A
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Elli
4 months ago
A
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Shanda
5 months ago
Option B seems like a lot of work. Why not just do one hypertuning job and let Vertex AI handle the different architectures?
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Robt
5 months ago
I agree with German. Running one hypertuning job with conditional hyperparameters seems like the best approach.
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Adaline
5 months 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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Sina
4 months ago
True, but option C also ensures that the hyperparameters are optimized based on the selected architecture.
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Kristal
4 months ago
That's a good point. Maybe running separate jobs for linear regression and DNN could give us a clearer picture.
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Billye
4 months ago
But wouldn't it be better to compare the two architectures separately like in option B?
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Thea
4 months ago
I agree, option C seems like the most logical choice.
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Precious
4 months ago
Yes, setting the hyperparameters as conditional based on the training method can help in finding the best combination for minimizing training loss.
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Bev
5 months ago
I agree, option C seems like the most efficient way to optimize the model architecture and hyperparameters.
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German
5 months ago
That makes sense. We can optimize both model architecture and hyperparameters that way.
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Jamal
5 months ago
I disagree. We should run one hypertuning job for 100 trials and set conditional hyperparameters.
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German
6 months ago
I think we should run two separate hypertuning jobs to compare linear regression and DNN.
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