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Google Professional Machine Learning Engineer Exam - Topic 3 Question 93 Discussion

You have recently developed a custom model for image classification by using a neural network. You need to automatically identify the values for learning rate, number of layers, and kernel size. To do this, you plan to run multiple jobs in parallel to identify the parameters that optimize performance. You want to minimize custom code development and infrastructure management. What should you do?
D) Create a Vertex Al hyperparameter tuning job.
A) Create a Vertex Al pipeline that runs different model training jobs in parallel.
B) Train an AutoML image classification model.
C) Create a custom training job that uses the Vertex Al Vizier SDK for parameter optimization.

Google Professional Machine Learning Engineer Exam - Topic 3 Question 93 Discussion

Actual exam question for Google's Professional Machine Learning Engineer exam
Question #: 93
Topic #: 3
[All Professional Machine Learning Engineer Questions]

You have recently developed a custom model for image classification by using a neural network. You need to automatically identify the values for learning rate, number of layers, and kernel size. To do this, you plan to run multiple jobs in parallel to identify the parameters that optimize performance. You want to minimize custom code development and infrastructure management. What should you do?

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Suggested Answer: D

by Luis at Nov 19, 2024, 12:38 AM

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Mirta
9 months ago
B is good, but I prefer more control over the model.
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Albina
9 months ago
C seems like a solid choice for custom needs.
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Daniela
10 months ago
Wait, can AutoML really handle all that? Sounds too easy!
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Augustine
10 months ago
I think D might be more efficient for hyperparameter tuning.
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Annmarie
10 months ago
A is the way to go for parallel jobs!
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Salley
10 months ago
I think option D about hyperparameter tuning is what we covered in class, and it seems like a solid approach for optimizing performance.
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Louvenia
11 months ago
I feel like training an AutoML model could save time, but it might not give us the control we need over the parameters.
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Cyril
11 months ago
I think option C sounds familiar; we practiced using the Vizier SDK for tuning parameters, but I can't recall the specifics.
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Harrison
11 months ago
I remember we discussed using Vertex AI for parallel jobs, but I'm not sure if it's the best choice here.
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Willow
11 months ago
I'm feeling pretty confident about this one. Creating a Vertex AI pipeline to run the model training jobs in parallel is definitely the way to go. That will save me a ton of time and effort compared to managing everything manually.
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Odelia
11 months ago
Option C using the Vertex AI Vizier SDK seems like a good way to have more control over the parameter optimization process. But I'm not as familiar with that tool, so I'll need to do some research to see if it's the right fit.
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Queen
11 months ago
Hmm, I'm a bit confused on the best approach here. Should I go with the Vertex AI pipeline or the hyperparameter tuning job? I'll need to review the details of each option to decide.
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Rochell
11 months ago
This looks like a great opportunity to use Vertex AI to optimize my model parameters. I think option A or D would be the best approach to minimize custom code and infrastructure management.
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Ernie
2 years ago
Wait, did someone say 'kernel size'? I'm picturing giant model kernels the size of basketballs. Now, that's what I call 'deep learning'!
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Celestina
2 years ago
D) Create a Vertex AI hyperparameter tuning job.
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Pilar
2 years ago
C) Create a custom training job that uses the Vertex AI Vizier SDK for parameter optimization.
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Sanjuana
2 years ago
B) Train an AutoML image classification model.
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William
2 years ago
A) Create a Vertex AI pipeline that runs different model training jobs in parallel.
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Shay
2 years ago
Hyperparameter tuning is where it's at! Just let the AI do its thing while I sit back and enjoy my coffee. Easy peasy!
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Isadora
2 years ago
Hyperparameter tuning is the way to go! Let the AI handle it while I relax.
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Jaime
2 years ago
D) Create a Vertex AI hyperparameter tuning job.
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Clay
2 years ago
B) Train an AutoML image classification model.
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Cecily
2 years ago
A) Create a Vertex AI pipeline that runs different model training jobs in parallel.
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Lucina
2 years ago
I prefer creating a custom training job using the Vertex AI Vizier SDK for parameter optimization. It gives us more control over the process.
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Galen
2 years ago
Haha, Vizier SDK for parameter optimization? Sounds like a one-way ticket to Complexity Town. I'll stick with the Vertex AI pipeline, thanks.
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Tandra
2 years ago
AuTandraL is tempting, but I want more control over my model. Gotta go with D - Vertex AI hyperparameter tuning for the win!
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Ciara
2 years ago
C) Create a custom training job that uses the Vertex AI Vizier SDK for parameter optimization.
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Sharika
2 years ago
A) Create a Vertex AI pipeline that runs different model training jobs in parallel.
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Rosann
2 years ago
D) Create a Vertex AI hyperparameter tuning job.
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Eleni
2 years ago
I agree with Kaitlyn, it will help us optimize performance without much custom code development.
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Kaitlyn
2 years ago
I think we should create a Vertex AI pipeline to run different model training jobs in parallel.
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Renay
2 years ago
Vertex AI pipeline sounds like the way to go! Minimal custom code and easy infrastructure management - sign me up!
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Graciela
2 years ago
D) Create a Vertex AI hyperparameter tuning job.
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Lawanda
2 years ago
C) Create a custom training job that uses the Vertex AI Vizier SDK for parameter optimization.
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Frankie
2 years ago
B) Train an AutoML image classification model.
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Candida
2 years ago
D) Create a Vertex AI hyperparameter tuning job.
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Carol
2 years ago
C) Create a custom training job that uses the Vertex AI Vizier SDK for parameter optimization.
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Carlota
2 years ago
A) Create a Vertex AI pipeline that runs different model training jobs in parallel.
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Ronny
2 years ago
B) Train an AutoML image classification model.
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Elli
2 years ago
A) Create a Vertex AI pipeline that runs different model training jobs in parallel.
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