Oracle Exam 1Z0-1127-24 Topic 3 Question 16 Discussion

Actual exam question for Oracle's 1Z0-1127-24 exam

Question #: 16
Topic #: 3

[All 1Z0-1127-24 Questions]

When should you use the T-Few fine-tuning method for training a model?

AFor complicated semantical undemanding improvement

BFor models that require their own hosting dedicated Al duster

CFor data sets with a few thousand samples or less

DFor data sets with hundreds of thousands to millions of samples

Show Suggested Answer

Suggested Answer: D

by Brynn at Sep 18, 2024, 05:20 AM

Limited Time Offer

25%

Off

Get Premium 1Z0-1127-24 Questions as Interactive Web-Based Practice Test or PDF

Contribute your Thoughts:

Submit Cancel

Elizabeth

2 months ago

I'm feeling option C as well. T-Few is all about fine-tuning with limited data, so small datasets make the most sense. Although, an 'AI cluster' does sound pretty fancy!

upvoted 0 times

Mireya

1 months ago

An 'AI cluster' does sound fancy, but for T-Few, small datasets are key.

upvoted 0 times

...

Basilia

1 months ago

Yeah, I agree. It's all about fine-tuning with limited data.

upvoted 0 times

...

Lacey

2 months ago

I think option C is the way to go. T-Few is perfect for small datasets.

upvoted 0 times

...

Corazon

2 months ago

Haha, 'semantical undemanding improvement'? What kind of corporate jargon is that? I'm guessing A is not the right answer. Let's go with C, it sounds the most straightforward.

upvoted 0 times

Carlton

1 months ago

Great choice, C is the correct answer for using the T-Few fine-tuning method.

upvoted 0 times

...

Rory

2 months ago

Definitely, C seems like the most straightforward option for the T-Few fine-tuning method.

upvoted 0 times

...

Geraldo

2 months ago

Yeah, C makes sense. It's for data sets with a few thousand samples or less.

upvoted 0 times

...

Glenna

2 months ago

I agree, 'semantical undemanding improvement' sounds like a mouthful. Let's go with C.

upvoted 0 times

...

Stevie

2 months ago

But what about models that require their own hosting dedicated Al duster? Shouldn't we consider that too?

upvoted 0 times

...

Peter

3 months ago

Hmm, I'm torn between B and C. I know T-Few is good for small datasets, but what's this about hosting a dedicated AI cluster? Is that really necessary? *scratches head*

upvoted 0 times

...

Royal

3 months ago

I'm going with D on this one. T-Few is designed to work with large datasets, right? Hundreds of thousands to millions of samples sounds about right to me.

upvoted 0 times

Louvenia

2 months ago

upvoted 0 times

...

Lacresha

2 months ago

upvoted 0 times

...

Dino

2 months ago

upvoted 0 times

...

Rodolfo

3 months ago

I agree with Lucia. It makes sense to use it for smaller data sets to avoid overfitting.

upvoted 0 times

...

Nan

3 months ago

I think option C is the correct answer. T-Few is great for small datasets, but I'm not sure about the hosting requirements. Does that mean we need a dedicated AI cluster or something?

upvoted 0 times

Reid

2 months ago

Yes, T-Few is specifically designed for smaller datasets to improve model performance.

upvoted 0 times

...

Galen

2 months ago

That makes sense. It's all about the size of the data when deciding to use the T-Few fine-tuning method.

upvoted 0 times

...

Paola

2 months ago

I believe you're right. T-Few is not about hosting requirements, but rather the size of the dataset.

upvoted 0 times

...

Della

3 months ago

Option C is correct. T-Few is indeed suitable for data sets with a few thousand samples or less.

upvoted 0 times

...

Lucia

3 months ago

I think we should use the T-Few fine-tuning method for data sets with a few thousand samples or less.

upvoted 0 times

...