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Oracle Exam 1Z0-1127-24 Topic 1 Question 5 Discussion

Actual exam question for Oracle's 1Z0-1127-24 exam
Question #: 5
Topic #: 1
[All 1Z0-1127-24 Questions]

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

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Suggested Answer: D

Contribute your Thoughts:

An
6 months ago
C can't be right, that's just regular fine-tuning. T-Few is all about selectively updating the model weights.
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Ilene
5 months ago
B) T-Few fine-tuning requires manual annotation of input-output pair.
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Kindra
5 months ago
C can't be right, that's just regular fine-tuning. T-Few is all about selectively updating the model weights.
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Malika
6 months ago
A) T-Few fine-tuning uses annotated data to adjust a fraction of model weights.
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Ivory
6 months ago
Haha, D is a good one. Unsupervised learning for annotation? That's like trying to herd cats with a laser pointer!
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Latia
6 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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Roxanne
6 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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Paz
6 months ago
I think B is correct. The annotation process for T-Few fine-tuning requires manual labeling of the input-output pairs, right?
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Tamesha
7 months ago
A seems like the right answer here. The key is that T-Few fine-tuning only adjusts a fraction of the model weights, not all of them.
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Alecia
5 months ago
Yeah, I agree. It's important to understand that not all weights are updated in this process.
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Felicidad
5 months ago
I think A is the correct answer. T-Few fine-tuning only adjusts a fraction of model weights.
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Avery
6 months ago
Yeah, that makes sense. It's important to understand that distinction.
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Lucina
6 months ago
I think A is the correct answer. T-Few fine-tuning adjusts only a fraction of model weights.
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Audry
6 months ago
You're correct. T-Few fine-tuning does indeed only adjust a fraction of the model weights.
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Iluminada
6 months ago
A) T-Few fine-tuning uses annotated data to adjust a fraction of model weights.
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