Google Exam Professional-Machine-Learning-Engineer Topic 4 Question 84 Discussion

Actual exam question for Google's Google Professional Machine Learning Engineer exam

Question #: 84
Topic #: 4

[All Google 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?

ACreate a Vertex Al pipeline that runs different model training jobs in parallel.

BTrain an AutoML image classification model.

CCreate a custom training job that uses the Vertex Al Vizier SDK for parameter optimization.

DCreate a Vertex Al hyperparameter tuning job.

Show Suggested Answer

Suggested Answer: D

by Dolores at Aug 08, 2024, 02:58 AM

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Tom

1 months ago

I think creating a custom training job using the Vertex AI Vizier SDK for parameter optimization would give us more control over the process.

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Samira

2 months ago

I prefer training an AutoML image classification model, it seems more straightforward.

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Christoper

2 months ago

I'm a big fan of the 'work smarter, not harder' approach, so I'm leaning towards Option D. Vertex AI Hyperparameter Tuning Job just seems like the most efficient way to tackle this challenge. Why reinvent the wheel, right?

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Destiny

27 days ago

Option D is the smart choice here. Let the automated job handle the parameter optimization for your custom model.

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Phuong

1 months ago

Definitely, why spend time manually tweaking parameters when you can use Vertex AI Hyperparameter Tuning Job to do it for you?

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Charlene

1 months ago

I agree, Option D seems like the most efficient choice. Let the system optimize the parameters for you.

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Caren

1 months ago

Option D sounds like the way to go. Let the Vertex AI Hyperparameter Tuning Job do the heavy lifting for you.

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Charlene

2 months ago

I agree with Chery, it will help us optimize performance efficiently.

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Chery

2 months ago

I think we should create a Vertex AI pipeline to run different model training jobs in parallel.

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Kristeen

2 months ago

Hold up, is that a Hyperparameter Tuning Job I see? Sounds like the perfect lazy person's solution to this problem. Optimize my model with minimal effort? Yes, please!

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Dahlia

2 months ago

Hmm, Option C looks interesting. Using the Vertex AI Vizier SDK could give me more control over the process. But I'm feeling lazy, so I might just go with the Hyperparameter Tuning Job instead. Less work, more optimization!

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Leandro

2 months ago

Option B all the way! Training an AutoML image classification model is the easiest path to get this done without all the hassle. Plus, I heard the AutoML models can be pretty darn accurate these days.

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Vincent

1 months ago

Option B sounds like the best choice. AutoML models are accurate and easy to use.

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Keneth

1 months ago

B) AutoML models are accurate and easy to use.

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Kati

1 months ago

B) Train an AutoML image classification model.

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Rolande

1 months ago

A) Create a Vertex AI pipeline that runs different model training jobs in parallel.

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Rory

1 months ago

B) Train an AutoML image classification model.

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Bette

2 months ago

I think Option D is the way to go. Vertex AI Hyperparameter Tuning Job sounds like the perfect solution to automate the parameter optimization process. Minimal custom code development? Sign me up!

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