Deal of The Day! Hurry Up, Grab the Special Discount - Save 25% - Ends In 00:00:00 Coupon code: SAVE25
Welcome to Pass4Success

- Free Preparation Discussions

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?

Show Suggested Answer Hide 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.


Contribute your Thoughts:

Alex
11 days ago
Haha, D is just silly. Diffusion models are for images? Tell that to my ghost-written novel I generated with them!
upvoted 0 times
...
Desire
14 days ago
That's a good point, Gail. Text generation requires a different approach compared to image generation.
upvoted 0 times
...
Marion
16 days 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.
upvoted 0 times
...
Gail
17 days ago
But isn't text also sequential in nature, making it harder to apply diffusion models?
upvoted 0 times
...
Kenneth
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.
upvoted 0 times
...
Gaston
24 days ago
I agree with Eden. Diffusion models are more suited for generating images.
upvoted 0 times
...
Eden
1 months ago
I think it's challenging because text representation is categorical unlike images.
upvoted 0 times
...
Kattie
1 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.
upvoted 0 times
Reuben
22 days ago
B) Because text is not categorical
upvoted 0 times
...
Jose
29 days ago
C) Because diffusion models work well for continuous data like images
upvoted 0 times
...
Leota
1 months ago
A) Because text representation is categorical unlike images
upvoted 0 times
...
...

Save Cancel
az-700  pass4success  az-104  200-301  200-201  cissp  350-401  350-201  350-501  350-601  350-801  350-901  az-720  az-305  pl-300  

Warning: Cannot modify header information - headers already sent by (output started at /pass.php:70) in /pass.php on line 77