The correct option is Limited interpretability of the model's image creation process. This limitation is most likely to cause challenges when executives ask for clear reasoning behind why certain elements appear in generated visuals.

Diffusion models construct images by iteratively denoising random noise and the model's internal decisions are distributed across many sampling steps. That process is powerful yet opaque and it is hard to provide a simple step by step explanation for how a specific attribute emerged which complicates governance and brand accountability.

Consistently deterministic images for the same prompt is incorrect because diffusion models are inherently stochastic and the same prompt can produce different outputs depending on sampling and random seeds.

Limited ability to scale GPU training capacity on AWS is incorrect because cloud providers such as AWS offer scalable GPU resources including Amazon SageMaker and EC2 P family instances that support large scale training and inference.

Inability to use text and image modalities together is incorrect because many diffusion based systems support multimodal conditioning and text to image workflows.

Full AWS Practitioner Certification Question