Oracle Exam 1Z0-1127-24 Topic 3 Question 17 Discussion

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

Question #: 17
Topic #: 3

[All 1Z0-1127-24 Questions]

Which is a key characteristic of the annotation process used in T-Few fine-tuning?

AT-Few fine-tuning uses annotated data to adjust a fraction of model weights.

BT-Few fine-tuning requires manual annotation of input-output pain.

CT- Few fine-tuning involves updating the weights of all layers in the model.

DT-Few fine-tuning relies on unsupervised learning techniques for annotation.

Show Suggested Answer

Suggested Answer: D

by Marge at Sep 19, 2024, 04:22 PM

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Valentin

2 months ago

Wait, if they're using T-Few, doesn't that mean they're too lazy to do the full annotation? Option D is the way to go!

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Corrina

1 months ago

True, T-Few fine-tuning might be a shortcut to avoid manual annotation like in option B.

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Carmela

1 months ago

But option A also sounds plausible, adjusting only a fraction of model weights with annotated data.

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Queenie

1 months ago

I think option D makes sense, using unsupervised learning for annotation seems efficient.

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Audrie

2 months ago

Hmm, I'm not convinced. D sounds more plausible - T-Few fine-tuning relies on unsupervised learning for annotation.

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Berry

1 months ago

User 3: I agree with Berry, C seems more accurate for T-Few fine-tuning.

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Sabra

1 months ago

User 2: Sabra, I think it's C) T-Few fine-tuning involves updating the weights of all layers in the model.

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Kristeen

2 months ago

A) T-Few fine-tuning uses annotated data to adjust a fraction of model weights.

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Merissa

2 months ago

No way, it's definitely C. T-Few fine-tuning updates all the layers in the model, not just a fraction.

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Brynn

1 months ago

I agree with you, it's definitely C. T-Few fine-tuning involves updating the weights of all layers in the model.

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Cyndy

1 months ago

I'm pretty sure it's B. T-Few fine-tuning requires manual annotation of input-output pairs.

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Orville

2 months ago

I disagree, it's actually C. T-Few fine-tuning updates all layers in the model.

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Benton

2 months ago

I think it's A. T-Few fine-tuning adjusts a fraction of model weights.

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Yuki

3 months ago

B seems like the right answer to me. T-Few fine-tuning requires manual annotation of input-output pairs.

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Davida

2 months ago

It's important to have accurate annotations for the fine-tuning process to be effective.

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Lorrie

2 months ago

Yes, you're right. That's a key characteristic of the annotation process in T-Few fine-tuning.

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Jeanice

2 months ago

I think B is correct. T-Few fine-tuning does require manual annotation of input-output pairs.

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Naomi

3 months ago

I think the answer is A. T-Few fine-tuning adjusts a fraction of the model weights using annotated data.

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Tamera

2 months ago

Great, so the key characteristic is indeed using annotated data to adjust model weights.

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Rashad

2 months ago

No, T-Few fine-tuning does not require manual annotation of input-output pairs.

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Annmarie

2 months ago

Do you think manual annotation of input-output pairs is necessary for T-Few fine-tuning?

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Judy

2 months ago

A) T-Few fine-tuning uses annotated data to adjust a fraction of model weights.

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Gail

3 months ago

I'm not sure, but I think B) T-Few fine-tuning requires manual annotation of input-output pair makes more sense.

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Malinda

3 months ago

I disagree, I believe it's C) T-Few fine-tuning involves updating the weights of all layers in the model.

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Gertude

3 months ago

I think the key characteristic is A) T-Few fine-tuning uses annotated data to adjust a fraction of model weights.

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