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

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?
C) Run one hypertuning job for 100 trials Set num_hidden_layers and learning_rate as conditional hyperparameters based on their parent hyperparameter training method.
A) Run 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
B) Run 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
D) Run 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

Google Professional Machine Learning Engineer Exam - 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

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

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Deeanna
7 months ago
Is it really necessary to run 100 trials? That seems excessive!
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Stephen
8 months ago
I disagree, A could lead to overfitting with one job.
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Sanjuana
8 months ago
Wait, why not just do A? Seems simpler!
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Vicky
8 months ago
B sounds good too, but splitting trials might waste resources.
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Bettina
8 months ago
I think option C makes the most sense with conditional hyperparameters.
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Shenika
9 months ago
I recall that selecting the best architecture first and then fine-tuning it later could be a solid strategy, which makes option D appealing, but I’m unsure about the trial distribution.
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Alecia
9 months ago
I practiced a similar question where we had to decide on hyperparameter settings based on the model type. I feel like option A could be a good choice, but I'm hesitant about the conditional setup.
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Gracie
9 months ago
I think running two separate hypertuning jobs might give a clearer comparison between the linear regression and DNN models, but I wonder if that would be the most efficient use of trials.
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Harrison
9 months ago
I remember that using conditional hyperparameters can help streamline the tuning process, but I'm not sure if it's necessary to set both learning_rate and num_hidden_layers as conditional.
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Fredric
9 months ago
This is a tricky one, but I think I've got a strategy. I'd go with option D - run a single hypertuning job with training_method as the hyperparameter, then select the best-performing architecture and further tune its hyperparameters. That way, I can focus my efforts on the most promising model and really optimize its performance.
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Emelda
9 months ago
Okay, I think I've got a handle on this. The key is to use conditional hyperparameters to explore the different model architectures effectively. Option C seems like the way to go - run a single hypertuning job with num_hidden_layers and learning_rate as conditional hyperparameters. That should give us the best results without wasting too much time.
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Jose
9 months ago
I'm a bit confused by the question. Do we really need to run two separate hypertuning jobs? Wouldn't it be more efficient to just do one job and let Vertex AI handle the conditional hyperparameters? I'm leaning towards option C, but I'll need to think it through a bit more.
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Mona
9 months ago
Hmm, this seems like a tricky one. I think I'd go with option C - running a single hypertuning job with num_hidden_layers and learning_rate as conditional hyperparameters based on the training_method. That way, I can explore the hyperparameter space more efficiently and find the best combination.
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Natalya
9 months ago
I'm feeling pretty confident about this one. The code seems straightforward, and I think I know the right approach to solving it.
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Ivette
9 months ago
Hmm, the information provided seems a bit complex. I'll need to walk through the numbers step-by-step to determine the correct long-term capital loss.
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Demetra
10 months ago
I'm not totally sure about this one. I'll have to think it through carefully.
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Carin
2 years 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
2 years 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
2 years 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
2 years ago
D
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Gussie
2 years ago
B
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Tamekia
2 years ago
A
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Kirk
2 years ago
D
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Nelida
2 years ago
C
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Dorinda
2 years ago
B
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Arlene
2 years ago
A
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Elli
2 years ago
A
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Shanda
2 years 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
2 years ago
I agree with German. Running one hypertuning job with conditional hyperparameters seems like the best approach.
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Adaline
2 years 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
2 years ago
True, but option C also ensures that the hyperparameters are optimized based on the selected architecture.
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Kristal
2 years 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
2 years ago
But wouldn't it be better to compare the two architectures separately like in option B?
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Thea
2 years ago
I agree, option C seems like the most logical choice.
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Precious
2 years 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
2 years ago
I agree, option C seems like the most efficient way to optimize the model architecture and hyperparameters.
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German
2 years ago
That makes sense. We can optimize both model architecture and hyperparameters that way.
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Jamal
2 years ago
I disagree. We should run one hypertuning job for 100 trials and set conditional hyperparameters.
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German
2 years ago
I think we should run two separate hypertuning jobs to compare linear regression and DNN.
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