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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?

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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.


Contribute your Thoughts:

Gaston
6 hours ago
I agree with Eden. Diffusion models are more suited for generating images.
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Eden
7 days ago
I think it's challenging because text representation is categorical unlike images.
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Kattie
15 days 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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Jose
5 days ago
C) Because diffusion models work well for continuous data like images
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Leota
6 days ago
A) Because text representation is categorical unlike images
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