Google Exam Professional Data Engineer Topic 3 Question 70 Discussion

Actual exam question for Google's Professional Data Engineer exam

Question #: 70
Topic #: 3

[All Professional Data Engineer Questions]

Which of the following are examples of hyperparameters? (Select 2 answers.)

ANumber of hidden layers

BNumber of nodes in each hidden layer

CBiases

DWeights

Show Suggested Answer

Suggested Answer: C

by Leslie at Sep 02, 2023, 10:48 PM

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Emilio

17 days ago

I'm going with A and B. Gotta love those hidden layers and node counts! They're like the dials on a machine learning control panel.

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Tabetha

20 days ago

Haha, this is a tricky one! I bet the exam creators just wanted to see if we can distinguish between the model's parameters and its hyperparameters. Time to brush up on my ML terminology!

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Lizbeth

26 days ago

Wait, I thought hyperparameters were external to the model, like the learning rate or the batch size. Isn't that what we're supposed to be looking for here?

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Anjelica

8 days ago

B) Number of nodes in each hidden layer

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Gerri

21 days ago

A) Number of hidden layers

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Georgeanna

2 months ago

I think biases and weights are also hyperparameters because they are set before training the model.

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Brent

2 months ago

I agree with Tawanna, hyperparameters include the number of hidden layers and nodes in each layer.

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Frederic

2 months ago

I'm pretty sure C and D are also hyperparameters. Biases and weights are part of the model's architecture and can be adjusted during the training process.

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Gilma

13 hours ago

I agree, adjusting biases and weights can have a significant impact on the model's learning process.

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Salley

3 days ago

Yes, biases and weights are important hyperparameters that can affect the model's performance.

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Becky

4 days ago

I think you're right, biases and weights are also hyperparameters that can be tuned.

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Linn

5 days ago

D) Weights

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Dierdre

8 days ago

C) Biases

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Annmarie

12 days ago

B) Number of nodes in each hidden layer

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Eden

30 days ago

A) Number of hidden layers

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Tawanna

2 months ago

A) Number of hidden layers and B) Number of nodes in each hidden layer

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German

2 months ago

Hmm, I think A and B are the correct hyperparameters. The number of hidden layers and nodes in each layer are crucial hyperparameters that can be tuned to optimize the model's performance.

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