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]

How does the temperature setting in a decoding algorithm influence the probability distribution over the vocabulary?

AIncreasing the temperature flattens the distribution, allowing for more varied word choices.

BIncreasing the temperature removes the impact of the most likely word.

CTemperature has no effect on probability distribution; it only changes the speed of decoding.

DDecreasing the temperature broadens the distribution, making less likely words more probable.

Show Suggested Answer

Suggested Answer: D

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

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Valentin

8 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

8 months ago

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

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Carmela

8 months ago

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

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Queenie

8 months ago

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

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Audrie

9 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

8 months ago

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

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Sabra

8 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

8 months ago

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

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Merissa

9 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

8 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

8 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

8 months ago

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

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Benton

8 months ago

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

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Yuki

9 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

8 months ago

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

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Lorrie

8 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

9 months ago

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

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Naomi

9 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

8 months ago

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

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Rashad

8 months ago

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

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Annmarie

9 months ago

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

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Judy

9 months ago

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

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Gail

9 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

9 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

9 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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