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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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Augustine
6 days ago
I think D might be more efficient for hyperparameter tuning.
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Annmarie
12 days ago
A is the way to go for parallel jobs!
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Salley
17 days 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
23 days 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
28 days 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
1 month 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
1 month 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
1 month 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
1 month 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
1 month 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
11 months 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
10 months ago
D) Create a Vertex AI hyperparameter tuning job.
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Pilar
10 months ago
C) Create a custom training job that uses the Vertex AI Vizier SDK for parameter optimization.
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Sanjuana
11 months ago
B) Train an AutoML image classification model.
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William
11 months ago
A) Create a Vertex AI pipeline that runs different model training jobs in parallel.
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Shay
11 months 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
10 months ago
Hyperparameter tuning is the way to go! Let the AI handle it while I relax.
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Jaime
11 months ago
D) Create a Vertex AI hyperparameter tuning job.
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Clay
11 months ago
B) Train an AutoML image classification model.
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Cecily
11 months ago
A) Create a Vertex AI pipeline that runs different model training jobs in parallel.
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Lucina
12 months 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
12 months 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
12 months 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
11 months ago
C) Create a custom training job that uses the Vertex AI Vizier SDK for parameter optimization.
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Sharika
11 months ago
A) Create a Vertex AI pipeline that runs different model training jobs in parallel.
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Rosann
11 months ago
D) Create a Vertex AI hyperparameter tuning job.
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Eleni
12 months ago
I agree with Kaitlyn, it will help us optimize performance without much custom code development.
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Kaitlyn
12 months ago
I think we should create a Vertex AI pipeline to run different model training jobs in parallel.
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Renay
12 months 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
11 months ago
D) Create a Vertex AI hyperparameter tuning job.
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Lawanda
11 months ago
C) Create a custom training job that uses the Vertex AI Vizier SDK for parameter optimization.
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Frankie
11 months ago
B) Train an AutoML image classification model.
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Candida
11 months ago
D) Create a Vertex AI hyperparameter tuning job.
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Carol
11 months ago
C) Create a custom training job that uses the Vertex AI Vizier SDK for parameter optimization.
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Carlota
11 months ago
A) Create a Vertex AI pipeline that runs different model training jobs in parallel.
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Ronny
12 months ago
B) Train an AutoML image classification model.
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Elli
12 months ago
A) Create a Vertex AI pipeline that runs different model training jobs in parallel.
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