Career Essentials in Generative AI by Microsoft and LinkedIn Practice Test 2025 - Free Generative AI Practice Questions and Study Guide

Transformers excel in generating coherent sequences of text due to their underlying architecture and mechanisms, such as self-attention and feed-forward neural networks. This design allows them to effectively understand and generate human language by capturing contextual relationships between words over long ranges in a sequence.

In tasks such as language translation, text summarization, or creative writing, transformers can produce text that is contextually appropriate and grammatically correct, maintaining continuity and coherence throughout the generated material. This ability stems from their training on vast amounts of textual data, enabling them to learn nuances of language, style, and context.

The other options do not align with the primary capabilities of transformers. While they can be involved in image and video content generation, as seen in models like DALL-E or Vision Transformers, their core strength lies in text generation. Generating large datasets for analysis or producing randomized data outputs does not capitalize on the specialized strengths of transformer models, which are fundamentally designed for sequential and contextual understanding in language processing.