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

Limited Time Offer

25%

Off

Get Premium Professional Data Engineer Questions as Interactive Web-Based Practice Test or PDF

Contribute your Thoughts:

Submit Cancel

Emilio

1 months 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.

upvoted 0 times

Oliva

15 days ago

I agree, A and B are definitely hyperparameters that can be tuned.

upvoted 0 times

...

Tabetha

1 months 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!

upvoted 0 times

Sharmaine

3 days ago

A) Number of hidden layers

upvoted 0 times

...

Lizbeth

1 months 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?

upvoted 0 times

Anjelica

25 days ago

B) Number of nodes in each hidden layer

upvoted 0 times

...

Gerri

1 months ago

A) Number of hidden layers

upvoted 0 times

...

Georgeanna

2 months ago

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

upvoted 0 times

...

Brent

2 months ago

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

upvoted 0 times

...

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.

upvoted 0 times

Gilma

18 days ago

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

upvoted 0 times

...

Salley

20 days ago

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

upvoted 0 times

...

Becky

21 days ago

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

upvoted 0 times

...

Linn

22 days ago

D) Weights

upvoted 0 times

...

Dierdre

25 days ago

C) Biases

upvoted 0 times

...

Annmarie

30 days ago

B) Number of nodes in each hidden layer

upvoted 0 times

...

Eden

2 months ago

A) Number of hidden layers

upvoted 0 times

...

Tawanna

2 months ago

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

upvoted 0 times

...

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.

upvoted 0 times

...