Oracle Exam 1Z0-1127-25 Topic 4 Question 6 Discussion

Actual exam question for Oracle's 1Z0-1127-25 exam

Question #: 6
Topic #: 4

[All 1Z0-1127-25 Questions]

Why is it challenging to apply diffusion models to text generation?

ABecause text generation does not require complex models

BBecause text is not categorical

CBecause text representation is categorical unlike images

DBecause diffusion models can only produce images

Show Suggested Answer

Suggested Answer: C

Comprehensive and Detailed In-Depth Explanation=

Diffusion models, widely used for image generation, iteratively denoise data from noise to a structured output. Images are continuous (pixel values), while text is categorical (discrete tokens), making it challenging to apply diffusion directly to text, as the denoising process struggles with discrete spaces. This makes Option C correct. Option A is false---text generation can benefit from complex models. Option B is incorrect---text is categorical. Option D is wrong, as diffusion models aren't inherently image-only but are better suited to continuous data. Research adapts diffusion for text, but it's less straightforward.

: OCI 2025 Generative AI documentation likely discusses diffusion models under generative techniques, noting their image focus.

by Vincent at Mar 21, 2025, 05:09 AM

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Alex

2 months ago

Haha, D is just silly. Diffusion models are for images? Tell that to my ghost-written novel I generated with them!

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Tequila

1 months ago

C) Because text representation is categorical unlike images

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Fannie

1 months ago

A) Because text generation does not require complex models

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Desire

2 months ago

That's a good point, Gail. Text generation requires a different approach compared to image generation.

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Marion

2 months ago

I'd have to go with C as well. Trying to treat text like a continuous signal just doesn't seem like the right approach. Categorical representations are key.

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Gail

2 months ago

But isn't text also sequential in nature, making it harder to apply diffusion models?

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Kenneth

2 months ago

I'm not sure I agree with A. Even simple models can struggle with text generation. The complexity of language is a real challenge for any approach.

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Heike

17 days ago

I'm not sure I agree with A. Even simple models can struggle with text generation. The complexity of language is a real challenge for any approach.

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Gwen

18 days ago

B) Because text is not categorical

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Rolande

24 days ago

C) Because text representation is categorical unlike images

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Moon

1 months ago

I'm not sure I agree with A. Even simple models can struggle with text generation. The complexity of language is a real challenge for any approach.

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Selene

1 months ago

C) Because text representation is categorical unlike images

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Edgar

2 months ago

A) Because text generation does not require complex models

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Gaston

2 months ago

I agree with Eden. Diffusion models are more suited for generating images.

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Eden

3 months ago

I think it's challenging because text representation is categorical unlike images.

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Kattie

3 months ago

Option C seems correct to me. Diffusion models work well for continuous data like images, but text is inherently categorical, making it more challenging to apply them directly.

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Reuben

2 months ago

B) Because text is not categorical

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Jose

3 months ago

C) Because diffusion models work well for continuous data like images

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Leota

3 months ago

A) Because text representation is categorical unlike images

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